Oil and Gas Supply Modeling

NASA Astrophysics Data System (ADS)

Gass, S. I.

1982-05-01

The theoretical and applied state of the art of oil and gas supply models was discussed. The following areas were addressed: the realities of oil and gas supply, prediction of oil and gas production, problems in oil and gas modeling, resource appraisal procedures, forecasting field size and production, investment and production strategies, estimating cost and production schedules for undiscovered fields, production regulations, resource data, sensitivity analysis of forecasts, econometric analysis of resource depletion, oil and gas finding rates, and various models of oil and gas supply.

Motor Vehicle Demand Models : Assessment of the State of the Art and Directions for Future Research

DOT National Transportation Integrated Search

1981-04-01

The report provides an assessment of the current state of motor vehicle demand modeling. It includes a detailed evaluation of one leading large-scale econometric vehicle demand model, which is tested for both logical consistency and forecasting accur...

Long-term US energy outlook

DOE Office of Scientific and Technical Information (OSTI.GOV)

Friesen, G.

Chase Econometrics summarizes the assumptions underlying long-term US energy forecasts. To illustrate the uncertainty involved in forecasting for the period to the year 2000, they compare Chase Econometrics forecasts with some recent projections prepared by the DOE Office of Policy, Planning and Analysis for the annual National Energy Policy Plan supplement. Scenario B, the mid-range reference case, is emphasized. The purpose of providing Scenario B as well as Scenarios A and C as alternate cases is to show the sensitivity of oil price projections to small swings in energy demand. 4 tables.

Short-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

2018-04-01

Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

An econometric simulation model of income and electricity demand in Alaska's Railbelt, 1982-2022

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maddigan, R.J.; Hill, L.J.; Hamblin, D.M.

1987-01-01

This report describes the specification of-and forecasts derived from-the Alaska Railbelt Electricity Load, Macroeconomic (ARELM) model. ARELM was developed as an independent, modeling tool for the evaluation of the need for power from the Susitna Hydroelectric Project which has been proposed by the Alaska Power Authority. ARELM is an econometric simulation model consisting of 61 equations - 46 behavioral equations and 15 identities. The system includes two components: (1) ARELM-MACRO which is a system of equations that simulates the performance of both the total Alaskan and Railbelt macroeconomies and (2) ARELM-LOAD which projects electricity-related activity in the Alaskan Railbelt region.more » The modeling system is block recursive in the sense that forecasts of population, personal income, and employment in the Railbelt derived from ARELM-MACRO are used as explanatory variables in ARELM-LOAD to simulate electricity demand, the real average price of electricity, and the number of customers in the Railbelt. Three scenarios based on assumptions about the future price of crude oil are simulated and documented in the report. The simulations, which do not include the cost-of-power impacts of Susitna-based generation, show that the growth rate in Railbelt electricity load is between 2.5 and 2.7% over the 1982 to 2022 forecast period. The forecasting results are consistent with other projections of load growth in the region using different modeling approaches.« less

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 1. The Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 3. Appendices to the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 2. Simulation Analysis Using the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Time to death and the forecasting of macro-level health care expenditures: some further considerations.

PubMed

van Baal, Pieter H; Wong, Albert

2012-12-01

Although the effect of time to death (TTD) on health care expenditures (HCE) has been investigated using individual level data, the most profound implications of TTD have been for the forecasting of macro-level HCE. Here we estimate the TTD model using macro-level data from the Netherlands consisting of mortality rates and age- and gender-specific per capita health expenditures for the years 1981-2007. Forecasts for the years 2008-2020 of this macro-level TTD model were compared to forecasts that excluded TTD. Results revealed that the effect of TTD on HCE in our macro model was similar to those found in micro-econometric studies. As the inclusion of TTD pushed growth rate estimates from unidentified causes upwards, however, the two models' forecasts of HCE for the 2008-2020 were similar. We argue that including TTD, if modeled correctly, does not lower forecasts of HCE. Copyright © 2012 Elsevier B.V. All rights reserved.

Models for forecasting energy use in the US farm sector

NASA Astrophysics Data System (ADS)

Christensen, L. R.

1981-07-01

Econometric models were developed and estimated for the purpose of forecasting electricity and petroleum demand in US agriculture. A structural approach is pursued which takes account of the fact that the quantity demanded of any one input is a decision made in conjunction with other input decisions. Three different functional forms of varying degrees of complexity are specified for the structural cost function, which describes the cost of production as a function of the level of output and factor prices. Demand for materials (all purchased inputs) is derived from these models. A separate model which break this demand up into demand for the four components of materials is used to produce forecasts of electricity and petroleum is a stepwise manner.

Value of Weather Information in Cranberry Marketing Decisions.

NASA Astrophysics Data System (ADS)

Morzuch, Bernard J.; Willis, Cleve E.

1982-04-01

Econometric techniques are used to establish a functional relationship between cranberry yields and important precipitation, temperature, and sunshine variables. Crop forecasts are derived from the model and are used to establish posterior probabilities to be used in a Bayesian decision context pertaining to leasing space for the storage of the berries.

The value of information as applied to the Landsat Follow-on benefit-cost analysis

NASA Technical Reports Server (NTRS)

Wood, D. B.

1978-01-01

An econometric model was run to compare the current forecasting system with a hypothetical (Landsat Follow-on) space-based system. The baseline current system was a hybrid of USDA SRS domestic forecasts and the best known foreign data. The space-based system improved upon the present Landsat by the higher spatial resolution capability of the thematic mapper. This satellite system is a major improvement for foreign forecasts but no better than SRS for domestic forecasts. The benefit analysis was concentrated on the use of Landsat Follow-on to forecast world wheat production. Results showed that it was possible to quantify the value of satellite information and that there are significant benefits in more timely and accurate crop condition information.

Regional economic forecasting models: Suitability for use in the National Acid Precipitation Assessment Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

South, D.W.; McDonald, J.F.; Oakland, W.H.

1990-02-01

In preparation for the Phase 1 test runs of the National Acid Precipitation Assessment Program Task Group B (TG-B) emissions model set, the need arose to provide regional economic data directly to the sector models in the model set and to the Argonne Regionalization Activity Module (ARAM). Candidate regional economic models were reviewed, and the Data Resources, Inc. (DRI), model was selected. This review of models, conducted during 1984--1985, is documented in this report. Even though considerable time has elapsed since then, the model descriptions and critique contained in this report are still fairly accurate and the recommendations should stillmore » be valid. There have been, however, some significant changes: (1) two of the economic consulting firms whose models were reviewed, Chase Econometrics and Wharton Econometric Forecasting Associates, have merged, (2) the DRI Regional Information System (DRI/RIS) now constructs a regional measure of industrial value of shipments, which will be used as the industrial activity variable (instead of employment) in the Phase 2 scenario analyses, and (3) based on recommendations from the third-party review of the TG-B model set, price-sensitive regional equations were developed to provide inputs, not already produced by the DRI/RIS model, directly to the sector models, thus eliminating the function served by ARAM. 44 refs., 12 figs., 44 tabs.« less

39 CFR 3050.26 - Documentation of demand elasticities and volume forecasts.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Documentation of demand elasticities and volume forecasts. 3050.26 Section 3050.26 Postal Service POSTAL REGULATORY COMMISSION PERSONNEL PERIODIC REPORTING § 3050.26 Documentation of demand elasticities and volume forecasts. By January 20 of each year, the Postal Service shall provide econometric...

Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

PubMed Central

Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

PubMed

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

Stochastic Forecasting of Labor Supply and Population: An Integrated Model.

PubMed

Fuchs, Johann; Söhnlein, Doris; Weber, Brigitte; Weber, Enzo

2018-01-01

This paper presents a stochastic model to forecast the German population and labor supply until 2060. Within a cohort-component approach, our population forecast applies principal components analysis to birth, mortality, emigration, and immigration rates, which allows for the reduction of dimensionality and accounts for correlation of the rates. Labor force participation rates are estimated by means of an econometric time series approach. All time series are forecast by stochastic simulation using the bootstrap method. As our model also distinguishes between German and foreign nationals, different developments in fertility, migration, and labor participation could be predicted. The results show that even rising birth rates and high levels of immigration cannot break the basic demographic trend in the long run. An important finding from an endogenous modeling of emigration rates is that high net migration in the long run will be difficult to achieve. Our stochastic perspective suggests therefore a high probability of substantially decreasing the labor supply in Germany.

The Evolving Urban Community and Military Installations: A Dynamic Spatial Decision Support System for Sustainable Military Communities

DTIC Science & Technology

2007-01-01

focus on identifying growth by income and housing costs. These, and other models are focused on the city itself and deal with growth over the course...2. This model employs a set of econometric models to project future population, household, and employment. The landscape is gridded into one... model in LEAM (LEAMecon) forecasts changes in output, employment and income over time based on changes in the market, technology, productivity and

An Econometric Analysis of the Unemployment Insurance System in a Local Urban Labor Market. Final Report for September 1, 1973--September 30, 1974.

ERIC Educational Resources Information Center

Marston, Stephen Tilney

The study derives a model of the unemployment insurance (UI) system and its relationship to the labor market, estimates it with data from the Detroit Standard Metropolitan Statistical Area, and evaluates its potential use to forecast UI benefit amounts, UI insured unemployment, and UI exhaustions. It further uses the model to analyze policy issues…

Parameterized examination in econometrics

NASA Astrophysics Data System (ADS)

Malinova, Anna; Kyurkchiev, Vesselin; Spasov, Georgi

2018-01-01

The paper presents a parameterization of basic types of exam questions in Econometrics. This algorithm is used to automate and facilitate the process of examination, assessment and self-preparation of a large number of students. The proposed parameterization of testing questions reduces the time required to author tests and course assignments. It enables tutors to generate a large number of different but equivalent dynamic questions (with dynamic answers) on a certain topic, which are automatically assessed. The presented methods are implemented in DisPeL (Distributed Platform for e-Learning) and provide questions in the areas of filtering and smoothing of time-series data, forecasting, building and analysis of single-equation econometric models. Questions also cover elasticity, average and marginal characteristics, product and cost functions, measurement of monopoly power, supply, demand and equilibrium price, consumer and product surplus, etc. Several approaches are used to enable the required numerical computations in DisPeL - integration of third-party mathematical libraries, developing our own procedures from scratch, and wrapping our legacy math codes in order to modernize and reuse them.

Allocation of Future Federal Airport and Airway Costs.

DTIC Science & Technology

1986-12-01

attributable to users are allocated among them based upon Ramsey Pricing which minimizes the distortion in aviation markets resulting from the allocation of...the years following 1992, the producers price Uindex projections made by Wharton Econometric Forecasting . Associates1 were employed. This latter set...and on econometric cost estimation techniques. These are Volumes 3 and 5 respectively. 68 A(A A11 I FSZK7_ ODi Id Lin <j< .99 C-4 x\\ M LL- < P7 Pi0

Energy supply and demand modeling. (Latest citations from the NTIS bibliographic database). Published Search

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1994-01-01

The bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (Contains 250 citations and includes a subject term index and title list.)

Energy supply and demand modeling. (Latest citations from the NTIS data base). Published Search

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1992-10-01

The bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (Contains 250 citations and includes a subject term index and title list.)

Energy supply and demand modeling. (Latest citations from the NTIS bibliographic database). Published Search

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1994-12-01

The bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (Contains 250 citations and includes a subject term index and title list.)

Directory of Energy Information Administration Model Abstracts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1986-07-16

This directory partially fulfills the requirements of Section 8c, of the documentation order, which states in part that: The Office of Statistical Standards will annually publish an EIA document based on the collected abstracts and the appendices. This report contains brief statements about each model's title, acronym, purpose, and status, followed by more detailed information on characteristics, uses, and requirements. Sources for additional information are identified. All models active through March 1985 are included. The main body of this directory is an alphabetical list of all active EIA models. Appendix A identifies major EIA modeling systems and the models withinmore » these systems, and Appendix B identifies active EIA models by type (basic, auxiliary, and developing). EIA also leases models developed by proprietary software vendors. Documentation for these proprietary models is the responsibility of the companies from which they are leased. EIA has recently leased models from Chase Econometrics, Inc., Data Resources, Inc. (DRI), the Oak Ridge National Laboratory (ORNL), and Wharton Econometric Forecasting Associates (WEFA). Leased models are not abstracted here. The directory is intended for the use of energy and energy-policy analysts in the public and private sectors.« less

The economic value of remote sensing of earth resources from space: An ERTS overview and the value of continuity of service. Volume 3: Intensive use of living resources, agriculture. Part 3: The integrated impact of improved (ERS) information on US agricultural commodities

NASA Technical Reports Server (NTRS)

Seidel, A. D.

1974-01-01

The economic value of information produced by an assumed operational version of an earth resources survey satellite of the ERTS class is assessed. The theoretical capability of an ERTS system to provide improved agricultural forecasts is analyzed and this analysis is used as a reasonable input to the econometric methods derived by ECON. An econometric investigation into the markets for agricultural commodities is summarized. An overview of the effort including the objectives, scopes, and architecture of the analysis, and the estimation strategy employed is presented. The results and conclusions focus on the economic importance of improved crop forecasts, U.S. exports, and government policy operations. Several promising avenues of further investigation are suggested.

An economic model of the manufacturers' aircraft production and airline earnings potential, volume 3

NASA Technical Reports Server (NTRS)

Kneafsey, J. T.; Hill, R. M.

1978-01-01

A behavioral explanation of the process of technological change in the U. S. aircraft manufacturing and airline industries is presented. The model indicates the principal factors which influence the aircraft (airframe) manufacturers in researching, developing, constructing and promoting new aircraft technology; and the financial requirements which determine the delivery of new aircraft to the domestic trunk airlines. Following specification and calibration of the model, the types and numbers of new aircraft were estimated historically for each airline's fleet. Examples of possible applications of the model to forecasting an individual airline's future fleet also are provided. The functional form of the model is a composite which was derived from several preceding econometric models developed on the foundations of the economics of innovation, acquisition, and technological change and represents an important contribution to the improved understanding of the economic and financial requirements for aircraft selection and production. The model's primary application will be to forecast the future types and numbers of new aircraft required for each domestic airline's fleet.

Systems modeling and analysis for Saudi Arabian electric power requirements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Al-Mohawes, N.A.

This thesis addresses the long-range generation planning problem in Saudi Arabia up to the year 2000. The first part presents various models for electric energy consumption in the residential and industrial sectors. These models can be used by the decision makers for the purposes of policy analysis, evaluation, and forecasting. Forecasts of energy in each sector are obtained from two different models for each sector. These models are based on two forecasting techniques: (1) Hybrid econometric/time series model. The idea of adaptive smoothing was utilized to produce forecasts under several scenarios. (2) Box-Jenkins time series technique. Box-Jenkins models and forecastsmore » are developed for the monthly number of electric consumers and the monthly energy consumption per consumer. The results obtained indicate that high energy consumption is expected during the coming two decades which necessitate serious energy assessment and optimization. Optimization of a mix of energy sources was considered using the group multiattribute utility (MAU) function. The results of MAU for three classes of decision makers (managerial, technical, and consumers) are developed through personal interactions. The computer package WASP was also used to develop a tentative optimum plan. According to this plan, four heavy-water nuclear power plants (800 MW) and four light-water nuclear power plants (1200 MW) have to be introduced by the year 2000 in addition to sixteen oil-fired power plants (400 MW) and nine gas turbines (100 MW).« less

Energy supply and demand modeling. February 1985-March 1988 (A Bibliography from the NTIS data base). Report for February 1985-March 1988

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1990-06-01

This bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (This updated bibliography contains 201 citations, none of which are new entries to the previous edition.)

Energy supply and demand modeling. February 1985-March 1988 (Citations from the NTIS data base). Report for February 1985-March 1988

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1988-04-01

This bibliography contains citations concerning the utilization of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long-term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (This updated bibliography contains 201 citations, 129 of which are new entries to the previous edition.)

Energy supply and demand modeling. April 1988-June 1990 (A Bibliography from the NTIS data base). Report for April 1988-June 1990

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1990-06-01

This bibliography contains citations concerning the use of mathematical models in trend analysis and forecasting of energy supply and demand factors. Models are presented for the industrial, transportation, and residential sectors. Aspects of long term energy strategies and markets are discussed at the global, national, state, and regional levels. Energy demand and pricing, and econometrics of energy, are explored for electric utilities and natural resources, such as coal, oil, and natural gas. Energy resources are modeled both for fuel usage and for reserves. (This updated bibliography contains 200 citations, all of which are new entries to the previous edition.)

With string model to time series forecasting

NASA Astrophysics Data System (ADS)

Pinčák, Richard; Bartoš, Erik

2015-10-01

Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.

Directory of Energy Information Administration model abstracts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1987-08-11

This report contains brief statements from the model managers about each model's title, acronym, purpose, and status, followed by more detailed information on characteristics, uses, and requirements. Sources for additional information are identified. All models ''active'' through March 1987 are included. The main body of this directory is an alphabetical list of all active EIA models. Appendix A identifies major EIA modeling systems and the models within these systems, and Appendix B identifies active EIA models by type (basic, auxiliary, and developing). A basic model is one designated by the EIA Administrator as being sufficiently important to require sustained supportmore » and public scrutiny. An auxiliary model is one designated by the EIA Administrator as being used only occasionally in analyses, and therefore requires minimal levels of documentation. A developing model is one designated by the EIA Administrator as being under development and yet of sufficient interest to require a basic level of documentation at a future date. EIA also leases models developed by proprietary software vendors. Documentation for these ''proprietary'' models is the responsibility of the companies from which they are leased. EIA has recently leased models from Chase Econometrics, Inc., Data Resources, Inc. (DRI), the Oak Ridge National Laboratory (ORNL), and Wharton Econometric Forecasting Associates (WEFA). Leased models are not abstracted here. The directory is intended for the use of energy and energy-policy analysts in the public and private sectors.« less

Benefits to world agriculture through remote sensing

NASA Technical Reports Server (NTRS)

Buffalano, A. C.; Kochanowski, P.

1976-01-01

Remote sensing of agricultural land permits crop classification and mensuration which can lead to improved forecasts of production. This technique is particularly important for nations which do not already have an accurate agricultural reporting system. Better forecasts have important economic effects. International grain traders can make better decisions about when to store, buy, and sell. Farmers can make better planting decisions by taking advantage of production estimates for areas out of phase with their own agricultural calendar. World economic benefits will accrue to both buyers and sellers because of increased food supply and price stabilization. This paper reviews the econometric models used to establish this scenario and estimates the dollar value of benefits for world wheat as 200 million dollars annually for the United States and 300 to 400 million dollars annually for the rest of the world.

Demand modelling of passenger air travel: An analysis and extension, volume 2

NASA Technical Reports Server (NTRS)

Jacobson, I. D.

1978-01-01

Previous intercity travel demand models in terms of their ability to predict air travel in a useful way and the need for disaggregation in the approach to demand modelling are evaluated. The viability of incorporating non-conventional factors (i.e. non-econometric, such as time and cost) in travel demand forecasting models are determined. The investigation of existing models is carried out in order to provide insight into their strong points and shortcomings. The model is characterized as a market segmentation model. This is a consequence of the strengths of disaggregation and its natural evolution to a usable aggregate formulation. The need for this approach both pedagogically and mathematically is discussed. In addition this volume contains two appendices which should prove useful to the non-specialist in the area.

The development and utilization of solar photovoltaic cells: An assessment of the potential for a new energy technology

NASA Technical Reports Server (NTRS)

Cyr, K. J.

1981-01-01

The Government set the goal of accelerating the adaptation of photovoltaics by reducing system costs to a competitive level and overcoming the technical, institutional, legal, environmental, and social barriers impeding the diffusion of photovoltaic technology. The technology of silicon solar arrays was examined and the status of development efforts are reviewed. The political, legal, economic, social, and environmental issues are discussed, and several methods for selecting development projects are described. A number of market forecasting techniques, including time trend, judgemental, and econometric methods, were reviewed, and the results of these models are presented.

Forecasting carbon dioxide emissions.

PubMed

Zhao, Xiaobing; Du, Ding

2015-09-01

This study extends the literature on forecasting carbon dioxide (CO2) emissions by applying the reduced-form econometrics approach of Schmalensee et al. (1998) to a more recent sample period, the post-1997 period. Using the post-1997 period is motivated by the observation that the strengthening pace of global climate policy may have been accelerated since 1997. Based on our parameter estimates, we project 25% reduction in CO2 emissions by 2050 according to an economic and population growth scenario that is more consistent with recent global trends. Our forecasts are conservative due to that we do not have sufficient data to fully take into account recent developments in the global economy. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Costs and Benefits of Deferred Giving.

ERIC Educational Resources Information Center

Fink, Norman S.; Metzler, Howard C.

It is argued in this book that while there can be a significant payoff for deferred giving programs, it is important to determine their cost effectiveness. Modern business methods of cost accounting, benefits analysis, and actuarial and econometric forecasting are applied to the Pomona College plan, whose study was supported by Lilly Endowment,…

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2010 CFR

2010-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2011 CFR

2011-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2012 CFR

2012-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2014 CFR

2014-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

10 CFR 905.11 - What must an IRP include?

Code of Federal Regulations, 2013 CFR

2013-01-01

... forecasting method, including but not limited to the time series, end-use, and econometric methods. The... projected durability of such savings measured over time; and must treat demand and supply resources on a... implement its IRP. (i) The IRP must state the time period that the action plan covers, and the action plan...

Uncertainty in forecasts of long-run economic growth.

PubMed

Christensen, P; Gillingham, K; Nordhaus, W

2018-05-22

Forecasts of long-run economic growth are critical inputs into policy decisions being made today on the economy and the environment. Despite its importance, there is a sparse literature on long-run forecasts of economic growth and the uncertainty in such forecasts. This study presents comprehensive probabilistic long-run projections of global and regional per-capita economic growth rates, comparing estimates from an expert survey and a low-frequency econometric approach. Our primary results suggest a median 2010-2100 global growth rate in per-capita gross domestic product of 2.1% per year, with a standard deviation (SD) of 1.1 percentage points, indicating substantially higher uncertainty than is implied in existing forecasts. The larger range of growth rates implies a greater likelihood of extreme climate change outcomes than is currently assumed and has important implications for social insurance programs in the United States.

MEDISE: A macroeconomic model for energy planning in Costa Rica

DOE Office of Scientific and Technical Information (OSTI.GOV)

Booth, S.R.; Leiva, C.L.

This report describes the development and results of MEDISE, an econometric macroeconomic model for energy planning in Costa Rica. The model is a simultaneous system of 19 equations that was constructed using ENERPLAN, an energy planning tool developed by the United Nations for use in developing countries. The equations were estimated using regression analysis on a data time series of 1966 to 1984. ENERPLAN's model solution package was used to obtain forecasts of 19 economic variables from 1985 to 2005. the modeling effort was conducted jointly by Los Alamos Central American Energy and Resources Project (CAP) personnel and the Energymore » Sector Directorate of Costa Rica during 1986. The CAP was funded by the US Agency for International Development. 6 refs., 3 figs., 11 tabs.« less

Predicting the impacts of new technology aircraft on international air transportation demand

NASA Technical Reports Server (NTRS)

Ausrotas, R. A.

1981-01-01

International air transportation to and from the United States was analyzed. Long term and short term effects and causes of travel are described. The applicability of econometric methods to forecast passenger travel is discussed. A nomograph is developed which shows the interaction of economic growth, airline yields, and quality of service in producing international traffic.

Directory of Energy Information Administration model abstracts 1988

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1988-01-01

This directory contains descriptions about each basic and auxiliary model, including the title, acronym, purpose, and type, followed by more detailed information on characteristics, uses, and requirements. For developing models, limited information is provided. Sources for additional information are identified. Included in this directory are 44 EIA models active as of February 1, 1988; 16 of which operate on personal computers. Models that run on personal computers are identified by ''PC'' as part of the acronyms. The main body of this directory is an alphabetical listing of all basic and auxiliary EIA models. Appendix A identifies major EIA modeling systemsmore » and the models within these systems, and Appendix B identifies EIA models by type (basic or auxiliary). Appendix C lists developing models and contact persons for those models. A basic model is one designated by the EIA Administrator as being sufficiently important to require sustained support and public scrutiny. An auxiliary model is one designated by the EIA Administrator as being used only occasionally in analyses, and therefore requires minimal levels of documentation. A developing model is one designated by the EIA Administrator as being under development and yet of sufficient interest to require a basic level of documentation at a future date. EIA also leases models developed by proprietary software vendors. Documentation for these ''proprietary'' models is the responsibility of the companies from which they are leased. EIA has recently leased models from Chase Econometrics, Inc., Data Resources, Inc. (DRI), the Oak Ridge National Laboratory (ORNL), and Wharton Econometric Forecasting Associates (WEFA). Leased models are not abstracted here.« less

Estimating the Regional Economic Significance of Airports

DTIC Science & Technology

1992-09-01

following three options for estimating induced impacts: the economic base model , an econometric model , and a regional input-output model . One approach to...limitations, however, the economic base model has been widely used for regional economic analysis. A second approach is to develop an econometric model of...analysis is the principal statistical tool used to estimate the economic relationships. Regional econometric models are capable of estimating a single

Forecasting the Revenues of Local Public Health Departments in the Shadows of the ‘Great Recession’

PubMed Central

Reschovsky, Andrew; Zahner, Susan J.

2015-01-01

Context The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the “Great Recession” on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy-makers. This study represents a novel research application for these econometric methods. Methods Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an eight year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in “base case” estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. Findings The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014 inflation-adjusted revenues will decline by $8 million, a reduction of 4.7 percent. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6 percent between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. Conclusions The impact of the Great Recession continues to haunt LHDs. Multi-year revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided. PMID:23531611

Forecasting the Revenues of Local Public Health Departments in the Shadows of the "Great Recession".

PubMed

Reschovsky, Andrew; Zahner, Susan J

2016-01-01

The ability of local health departments (LHD) to provide core public health services depends on a reliable stream of revenue from federal, state, and local governments. This study investigates the impact of the "Great Recession" on major sources of LHD revenues and develops a fiscal forecasting model to predict revenues to LHDs in one state over the period 2012 to 2014. Economic forecasting offers a new financial planning tool for LHD administrators and local government policy makers. This study represents a novel research application for these econometric methods. Detailed data on revenues by source for each LHD in Wisconsin were taken from annual surveys conducted by the Wisconsin Department of Health Services over an 8-year period (2002-2009). A forecasting strategy appropriate for each revenue source was developed resulting in "base case" estimates. An analysis of the sensitivity of these revenue forecasts to a set of alternative fiscal policies by the federal, state, and local governments was carried out. The model forecasts total LHD revenues in 2012 of $170.5 million (in 2010 dollars). By 2014, inflation-adjusted revenues will decline by $8 million, a reduction of 4.7%. Because of population growth, per capita real revenues of LHDs are forecast to decline by 6.6% between 2012 and 2014. There is a great deal of uncertainty about the future of federal funding in support of local public health. A doubling of the reductions in federal grants scheduled under current law would result in an additional $4.4 million decline in LHD revenues in 2014. The impact of the Great Recession continues to haunt LHDs. Multiyear revenue forecasting offers a new financial tool to help LHDs better plan for an environment of declining resources. New revenue sources are needed if sharp drops in public health service delivery are to be avoided.

The log-periodic-AR(1)-GARCH(1,1) model for financial crashes

NASA Astrophysics Data System (ADS)

Gazola, L.; Fernandes, C.; Pizzinga, A.; Riera, R.

2008-02-01

This paper intends to meet recent claims for the attainment of more rigorous statistical methodology within the econophysics literature. To this end, we consider an econometric approach to investigate the outcomes of the log-periodic model of price movements, which has been largely used to forecast financial crashes. In order to accomplish reliable statistical inference for unknown parameters, we incorporate an autoregressive dynamic and a conditional heteroskedasticity structure in the error term of the original model, yielding the log-periodic-AR(1)-GARCH(1,1) model. Both the original and the extended models are fitted to financial indices of U. S. market, namely S&P500 and NASDAQ. Our analysis reveal two main points: (i) the log-periodic-AR(1)-GARCH(1,1) model has residuals with better statistical properties and (ii) the estimation of the parameter concerning the time of the financial crash has been improved.

Forecasting fluid milk and cheese demands for the next decade.

PubMed

Schmit, T M; Kaiser, H M

2006-12-01

Predictions of future market demands and farm prices for dairy products are important determinants in developing marketing strategies and farm-production planning decisions. The objective of this report was to use current aggregate forecast data, combined with existing econometric models of demand and supply, to forecast retail demands for fluid milk and cheese and the supply and price of farm milk over the next decade. In doing so, we can investigate whether projections of population and consumer food-spending patterns will extend or alter current consumption trends and examine the implications of future generic advertising strategies for dairy products. To conduct the forecast simulations and appropriately allocate the farm milk supply to various uses, we used a partial equilibrium model of the US domestic dairy sector that segmented the industry into retail, wholesale, and farm markets. Model simulation results indicated that declines in retail per capita demand would persist but at a reduced rate from years past and that retail per capita demand for cheese would continue to grow and strengthen over the next decade. These predictions rely on expected changes in the size of populations of various ages, races, and ethnicities and on existing patterns of spending on food at home and away from home. The combined effect of these forecasted changes in demand levels was reflected in annualized growth in the total farm-milk supply that was similar to growth realized during the past few years. Although we expect nominal farm milk prices to increase over the next decade, we expect real prices (relative to assumed growth in feed costs) to remain relatively stable and show no increase until the end of the forecast period. Supplemental industry model simulations also suggested that net losses in producer revenues would result if only nominal levels of generic advertising spending were maintained in forthcoming years. In fact, if real generic advertising expenditures are increased relative to 2005 levels, returns to the investment in generic advertising can be improved. Specifically, each additional real dollar invested in generic advertising for fluid milk and cheese products over the forecast period would result in an additional 5.61 dollars in producer revenues.

Assessing Independent Variables Used in Econometric Modeling Forest Land Use or Land Cover Change: A Meta-Analysis

Treesearch

J Jeuck; F. Cubbage; R. Abt; R. Bardon; J. McCarter; J. Coulston; M. Renkow

2014-01-01

: We conducted a meta-analysis on 64 econometric models from 47 studies predicting forestland conversion to agriculture (F2A), forestland to development (F2D), forestland to non-forested (F2NF) and undeveloped (including forestland) to developed (U2D) land. Over 250 independent econometric variables were identified from 21 F2A models, 21 F2D models, 12 F2NF models, and...

State Labor Market Research Study: An Econometric Analysis of the Effects of Labor Subsidies.

ERIC Educational Resources Information Center

MacRae, C. Duncan; And Others

The report describes the construction, application, and theoretical implications of an econometric model depicting the effects of labor subsidies on the supply of workers in the U.S. Three papers deal with the following aspects of constructing the econometric model: (1) examination of equilibrium wages, employment, and earnings of primary and…

Mitigating randomness of consumer preferences under certain conditional choices

NASA Astrophysics Data System (ADS)

Bothos, John M. A.; Thanos, Konstantinos-Georgios; Papadopoulou, Eirini; Daveas, Stelios; Thomopoulos, Stelios C. A.

2017-05-01

Agent-based crowd behaviour consists a significant field of research that has drawn a lot of attention in recent years. Agent-based crowd simulation techniques have been used excessively to forecast the behaviour of larger or smaller crowds in terms of certain given conditions influenced by specific cognition models and behavioural rules and norms, imposed from the beginning. Our research employs conditional event algebra, statistical methodology and agent-based crowd simulation techniques in developing a behavioural econometric model about the selection of certain economic behaviour by a consumer that faces a spectre of potential choices when moving and acting in a multiplex mall. More specifically we try to analyse the influence of demographic, economic, social and cultural factors on the economic behaviour of a certain individual and then we try to link its behaviour with the general behaviour of the crowds of consumers in multiplex malls using agent-based crowd simulation techniques. We then run our model using Generalized Least Squares and Maximum Likelihood methods to come up with the most probable forecast estimations, regarding the agent's behaviour. Our model is indicative about the formation of consumers' spectre of choices in multiplex malls under the condition of predefined preferences and can be used as a guide for further research in this area.

The Role of the Manufacturer in Air Transportation Planning

NASA Technical Reports Server (NTRS)

Mackenzie, J.

1972-01-01

The role of the aircraft manufacturer in the airline industry is considered. The process is illustrated by using a fictitious airline as an example--that is, a case study approach with Mid-Coast Airways serving as the example. Both in slide form and with supporting papers, a brief history of the airline, a description of its route structure and a forecast based on econometric analysis are presented. Once the forecast rationale is explained, information outlines the requirements for additional aircraft and the application of new aircraft across the system using alternative fleet plan options. The fleet plan is translated into financial summaries which indicate the relative merit of alternative aircraft types or operating plans.

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

PubMed

Stránský, V; Thinová, L

2017-11-01

In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Regional demand forecasting and simulation model: user's manual. Task 4, final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parhizgari, A M

1978-09-25

The Department of Energy's Regional Demand Forecasting Model (RDFOR) is an econometric and simulation system designed to estimate annual fuel-sector-region specific consumption of energy for the US. Its purposes are to (1) provide the demand side of the Project Independence Evaluation System (PIES), (2) enhance our empirical insights into the structure of US energy demand, and (3) assist policymakers in their decisions on and formulations of various energy policies and/or scenarios. This report provides a self-contained user's manual for interpreting, utilizing, and implementing RDFOR simulation software packages. Chapters I and II present the theoretical structure and the simulation of RDFOR,more » respectively. Chapter III describes several potential scenarios which are (or have been) utilized in the RDFOR simulations. Chapter IV presents an overview of the complete software package utilized in simulation. Chapter V provides the detailed explanation and documentation of this package. The last chapter describes step-by-step implementation of the simulation package using the two scenarios detailed in Chapter III. The RDFOR model contains 14 fuels: gasoline, electricity, natural gas, distillate and residual fuels, liquid gases, jet fuel, coal, oil, petroleum products, asphalt, petroleum coke, metallurgical coal, and total fuels, spread over residential, commercial, industrial, and transportation sectors.« less

Econometrics of exhaustible resource supply: a theory and an application. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Epple, D.; Hansen, L.P.

1981-12-01

An econometric model of US oil and natural gas discoveries is developed in this study. The econometric model is explicitly derived as the solution to the problem of maximizing the expected discounted after tax present value of revenues net of exploration, development, and production costs. The model contains equations representing producers' formation of price expectations and separate equations giving producers' optimal exploration decisions contingent on expected prices. A procedure is developed for imposing resource base constraints (e.g., ultimate recovery estimates based on geological analysis) when estimating the econometric model. The model is estimated using aggregate post-war data for the Unitedmore » States. Production from a given addition to proved reserves is assumed to follow a negative exponential path, and additions of proved reserves from a given discovery are assumed to follow a negative exponential path. Annual discoveries of oil and natural gas are estimated as latent variables. These latent variables are the endogenous variables in the econometric model of oil and natural gas discoveries. The model is estimated without resource base constraints. The model is also estimated imposing the mean oil and natural gas ultimate recovery estimates of the US Geological Survey. Simulations through the year 2020 are reported for various future price regimes.« less

Legitimate Techniques for Improving the R-Square and Related Statistics of a Multiple Regression Model

DTIC Science & Technology

1981-01-01

explanatory variable has been ommitted. Ramsey (1974) has developed a rather interesting test for detecting specification errors using estimates of the...Peter. (1979) A Guide to Econometrics , Cambridge, MA: The MIT Press. Ramsey , J.B. (1974), "Classical Model Selection Through Specification Error... Tests ," in P. Zarembka, Ed. Frontiers in Econometrics , New York: Academia Press. Theil, Henri. (1971), Principles of Econometrics , New York: John Wiley

Identification and Inference for Econometric Models

NASA Astrophysics Data System (ADS)

Andrews, Donald W. K.; Stock, James H.

2005-07-01

This volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose new ones. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Econometric Models of U.S. Navy Career Petty Officer Retention.

DTIC Science & Technology

1981-06-01

PF AD-AL04 076 NAVAL POSTGRADUATE SCHOOL MONTEREY CA F/6 5/9 ECONO ETRIC MODELS OF U.S. NAVY CAREER PETTY OFFICER RETENTION.(Ul JUN 81 J J B PKO...THESIS D . ECONOMETRIC MODELS OF U. S. NAVY CAREER PETTY OFFICER RETENTION SML Vby John Joseph Bepko III June 1981 Thesis Advisor: George W. Thomas...DOCUMENTATION PACE 33703 coTu~rwc oEm 0419PsR 01N1911VT*48~ &GM01 1𔃻. 411CIP1SIMYS CATALOG IulmSIS Econometric Models of U. S. Navy Career Petty 1’ t h s s j

Factors influencing crime rates: an econometric analysis approach

NASA Astrophysics Data System (ADS)

Bothos, John M. A.; Thomopoulos, Stelios C. A.

2016-05-01

The scope of the present study is to research the dynamics that determine the commission of crimes in the US society. Our study is part of a model we are developing to understand urban crime dynamics and to enhance citizens' "perception of security" in large urban environments. The main targets of our research are to highlight dependence of crime rates on certain social and economic factors and basic elements of state anticrime policies. In conducting our research, we use as guides previous relevant studies on crime dependence, that have been performed with similar quantitative analyses in mind, regarding the dependence of crime on certain social and economic factors using statistics and econometric modelling. Our first approach consists of conceptual state space dynamic cross-sectional econometric models that incorporate a feedback loop that describes crime as a feedback process. In order to define dynamically the model variables, we use statistical analysis on crime records and on records about social and economic conditions and policing characteristics (like police force and policing results - crime arrests), to determine their influence as independent variables on crime, as the dependent variable of our model. The econometric models we apply in this first approach are an exponential log linear model and a logit model. In a second approach, we try to study the evolvement of violent crime through time in the US, independently as an autonomous social phenomenon, using autoregressive and moving average time-series econometric models. Our findings show that there are certain social and economic characteristics that affect the formation of crime rates in the US, either positively or negatively. Furthermore, the results of our time-series econometric modelling show that violent crime, viewed solely and independently as a social phenomenon, correlates with previous years crime rates and depends on the social and economic environment's conditions during previous years.

Cost Effectiveness Trade-Offs in Software Support Environment Standardization.

DTIC Science & Technology

1986-09-30

IIIIIEEEIIIIIE MiII I U..2 2 ma MICROCOPY RESOLUTION TEST CHART 911C FILE C y, o FINAL REPORT - September 30, 1986 G- TECHNION INTERNATIONAL, INC. Cost...Summary description of econometric model B-I C. Causal chain used as basis for model C-I D. Excerpts from [Wer185) D-1 LIST OF FIGURES S-1 USAF MCCR...Productivity cost drivers D-4 LIST OF TASL3$ I-1 Summary of Tangible Benefits in Econometric Equations 1-9 1-2 Summary of Tangible Costs in Econometric

Outputs as Educator Effectiveness in the United States: Shifting towards Political Accountability

ERIC Educational Resources Information Center

Piro, Jody S.; Mullen, Laurie

2013-01-01

The definition of educator effectiveness is being redefined by econometric modeling to evidence student achievement on standardized tests. While the reasons that econometric frameworks are in vogue are many, it is clear that the strength of such models lie in the quantifiable evidence of student learning. Current accountability models frame…

Empirical methods for modeling landscape change, ecosystem services, and biodiversity

Treesearch

David Lewis; Ralph Alig

2009-01-01

The purpose of this paper is to synthesize recent economics research aimed at integrating discrete-choice econometric models of land-use change with spatially-explicit landscape simulations and quantitative ecology. This research explicitly models changes in the spatial pattern of landscapes in two steps: 1) econometric estimation of parcel-scale transition...

Econometrics and Psychometrics: A Survey of Communalities

ERIC Educational Resources Information Center

Goldberger, Arthur S.

1971-01-01

Several themes which are common to both econometrics and psychometrics are surveyed. The themes are illustrated by reference to permanent income hypotheses, simultaneous equation models, adaptive expectations and partial adjustment schemes, and by reference to test score theory, factor analysis, and time-series models. (Author)

Transportation and socioeconomic impacts of bypasses on communities : an integrated synthesis of panel data, multilevel, and spatial econometric models with case studies.

DOT National Transportation Integrated Search

2011-09-21

Title: Transportation and Socioeconomic Impacts of Bypasses on Communities: An Integrated Synthesis of Panel Data, Multilevel, and Spatial Econometric Models with Case Studies. The title used at the start of this project was Transportation and Soc...

Improved (ERTS) information and its impact on U.S. markets for agricultural commodities: A quantitiative economic investigation of production, distribution and net export effects

NASA Technical Reports Server (NTRS)

1974-01-01

An econometric investigation into the markets for agricultural commodities is summarized. An overview of the effort including the objectives, scope, and architecture of the analysis and the estimation strategy employed is presented. The major empirical results and policy conclusions are set forth. These results and conclusions focus on the economic importance of improved crop forecasts, U.S. exports, and government policy operations. A number of promising avenues of further investigation are suggested.

The Anatomy of a Likely Donor: Econometric Evidence on Philanthropy to Higher Education

ERIC Educational Resources Information Center

Lara, Christen; Johnson, Daniel

2014-01-01

In 2011, philanthropic giving to higher education institutions totaled $30.3 billion, an 8.2% increase over the previous year. Roughly, 26% of those funds came from alumni donations. This article builds upon existing economic models to create an econometric model to explain and predict the pattern of alumni giving. We test the model using data…

An econometric model of the hardwood lumber market

Treesearch

William G. Luppold

1982-01-01

A recursive econometric model with causal flow originating from the demand relationship is used to analyze the effects of exogenous variables on quantity and price of hardwood lumber. Wage rates, interest rates, stumpage price, lumber exports, and price of lumber demanders' output were the major factors influencing quantities demanded and supplied and hardwood...

Econometric models of road use, accidents, and road investment decisions. Volume 2 : an econometric model of car ownership, road use, accidents, and their severity (Essay 3)

DOT National Transportation Integrated Search

1999-11-01

Using a fairly large cross-section/time-series data base, covering all provinces of Norway and all months between January 1973 and December 1994, we estimate non-linear (Box-Cox) regression equations explaining aggregate car ownership, road use, seat...

78 FR 41161 - Self-Regulatory Organizations; The Options Clearing Corporation; Notice of Filing of Advance...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-09

... behavior is included in the econometric models underlying STANS, time series of proportional changes in... included in the econometric models underlying STANS, time series of proportional changes in implied... calculate daily margin requirements. OCC has proposed at this time to clear only OTC Options on the S&P 500...

Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali

PubMed Central

Medina, Daniel C.; Findley, Sally E.; Guindo, Boubacar; Doumbia, Seydou

2007-01-01

Background Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. Methodology/Principal Findings In this longitudinal retrospective (01/1996–06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. Conclusions/Significance The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel. PMID:18030322

Forecasting non-stationary diarrhea, acute respiratory infection, and malaria time-series in Niono, Mali.

PubMed

Medina, Daniel C; Findley, Sally E; Guindo, Boubacar; Doumbia, Seydou

2007-11-21

Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. In this longitudinal retrospective (01/1996-06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel.

Bayesian Nonparametric Prediction and Statistical Inference

DTIC Science & Technology

1989-09-07

Kadane, J. (1980), "Bayesian decision theory and the sim- plification of models," in Evaluation of Econometric Models, J. Kmenta and J. Ramsey , eds...the random model and weighted least squares regression," in Evaluation of Econometric Models, ed. by J. Kmenta and J. Ramsey , Academic Press, 197-217...likelihood function. On the other hand, H. Jeffreys’s theory of hypothesis testing covers the most important situations in which the prior is not diffuse. See

Journal of Air Transportation, Volume 8, No. 2. Volume 8, No. 2

NASA Technical Reports Server (NTRS)

Bowen, Brent (Editor); Kabashkin, Igor (Editor); Nickerson, Jocelyn (Editor)

2003-01-01

The mission of the Journal of Air Transportation (JAT) is to provide the global community immediate key resource information in all areas of air transportation. This journal contains articles on the following:Fuel Consumption Modeling of a Transport Category Aircraft: A FlightOperationsQualityAssurance (F0QA) Analysis;Demand for Air Travel in the United States: Bottom-Up Econometric Estimation and Implications for Forecasts by Origin and Destination Pairs;Blind Flying on the Beam: Aeronautical Communication, Navigation and Surveillance: Its Origins and the Politics of Technology: Part I1 Political Oversight and Promotion;Blind Flying on the Beam: Aeronautical Communication, Navigation and Surveillance: Its Origins and the Politics of Technology: Part 111: Emerging Technologies;Ethics Education in University Aviation Management Programs in the US: Part Two B-Statistical Analysis of Current Practice;Integrating Human Factors into the Human-computer Interface: and How Best to Display Meteorological Information for Critical Aviation Decision-making and Performance.

Consumer Choice of E85 Denatured Ethanol Fuel Blend: Price Sensitivity and Cost of Limited Fuel Availability

DOE PAGES

Liu, Changzheng; Greene, David

2014-12-01

The promotion of greater use of E85, a fuel blend of 85% denatured ethanol, by flex-fuel vehicle owners is an important means of complying with the Renewable Fuel Standard 2. A good understanding of factors affecting E85 demand is necessary for effective policies that promote E85 and for developing models that forecast E85 sales in the United States. In this paper, the sensitivity of aggregate E85 demand to E85 and gasoline prices is estimated, as is the relative availability of E85 versus gasoline. The econometric analysis uses recent data from Minnesota, North Dakota, and Iowa. The more recent data allowmore » a better estimate of nonfleet demand and indicate that the market price elasticity of E85 choice is substantially higher than previously estimated.« less

The Model Analyst’s Toolkit: Scientific Model Development, Analysis, and Validation

DTIC Science & Technology

2015-02-20

being integrated within MAT, including Granger causality. Granger causality tests whether a data series helps when predicting future values of another...relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438. Granger, C. W. (1980). Testing ... testing dataset. This effort is described in Section 3.2. 3.1. Improvements in Granger Causality User Interface Various metrics of causality are

An econometric model of the U.S. pallet market

Treesearch

Albert T. Schuler; Walter B. Wallin

1979-01-01

A need for quantitative information on demand and price has been expressed by the pallet industry. In response to this, an econometric model of the aggregate U.S. pallet market was developed. Demand was found to be affected by real pallet price, industrial and food production levels, and slipsheet prices. Supply was affected by real price, housing starts lagged 1 year...

Analysis of environmental constraints on expanding reserves in current and future reservoirs in wetlands. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harder, B.J.

1995-03-01

Louisiana wetlands require careful management to allow exploitation of non-renewable resources without destroying renewable resources. Current regulatory requirements have been moderately successful in meeting this goal by restricting development in wetland habitats. Continuing public emphasis on reducing environmental impacts of resource development is causing regulators to reassess their regulations and operators to rethink their compliance strategies. We examined the regulatory system and found that reducing the number of applications required by going to a single application process and having a coherent map of the steps required for operations in wetland areas would reduce regulatory burdens. Incremental changes can be mademore » to regulations to allow one agency to be the lead for wetland permitting at minimal cost to operators. Operators need cost effective means of access that will reduce environmental impacts, decrease permitting time, and limit future liability. Regulators and industry must partner to develop incentive based regulations that can provide significant environmental impact reduction for minimal economic cost. In addition regulators need forecasts of future E&P trends to estimate the impact of future regulations. To determine future activity we attempted to survey potential operators when this approach was unsuccessful we created two econometric models of north and south Louisiana relating drilling activity, success ratio, and price to predict future wetland activity. Results of the econometric models indicate that environmental regulations have a small but statistically significant effect on drilling operations in wetland areas of Louisiana. We examined current wetland practices and evaluated those practices comparing environmental versus economic costs and created a method for ranking the practices.« less

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

PubMed

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

PubMed Central

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

A Theory of Bayesian Data Analysis

DTIC Science & Technology

1989-10-10

and the sim- plification of models," in Evaluation of Econometric Models, J. Kmenta and J. 20 Ramsey , eds., Academic Press, 245-268. Edwards, W...Evaluation of Econometric Models, ed. by J. Kmenta and J. Ramsey , Academic Press, 197-217. Hill, Bruce M., (1980c), Review of Specification Searches, by E...also Hill (1970a, 1975a) for earlier thoughts the subject with regard to tests of significance, and Smith.(1986). The Baesi theory of tests of

An Applied Physicist Does Econometrics

NASA Astrophysics Data System (ADS)

Taff, L. G.

2010-02-01

The biggest problem those attempting to understand econometric data, via modeling, have is that economics has no F = ma. Without a theoretical underpinning, econometricians have no way to build a good model to fit observations to. Physicists do, and when F = ma failed, we knew it. Still desiring to comprehend econometric data, applied economists turn to mis-applying probability theory---especially with regard to the assumptions concerning random errors---and choosing extremely simplistic analytical formulations of inter-relationships. This introduces model bias to an unknown degree. An applied physicist, used to having to match observations to a numerical or analytical model with a firm theoretical basis, modify the model, re-perform the analysis, and then know why, and when, to delete ``outliers'', is at a considerable advantage when quantitatively analyzing econometric data. I treat two cases. One is to determine the household density distribution of total assets, annual income, age, level of education, race, and marital status. Each of these ``independent'' variables is highly correlated with every other but only current annual income and level of education follow a linear relationship. The other is to discover the functional dependence of total assets on the distribution of assets: total assets has an amazingly tight power law dependence on a quadratic function of portfolio composition. Who knew? )

Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting

PubMed Central

Aydin, Alev Dilek; Caliskan Cavdar, Seyma

2015-01-01

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method. PMID:26550010

Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting.

PubMed

Aydin, Alev Dilek; Caliskan Cavdar, Seyma

2015-01-01

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.

Reflections on Heckman and Pinto’s Causal Analysis After Haavelmo

DTIC Science & Technology

2013-11-01

Econometric Analysis , Cambridge University Press, 477–490, 1995. Halpern, J. (1998). Axiomatizing causal reasoning. In Uncertainty in Artificial...Models, Structural Models and Econometric Policy Evaluation. Elsevier B.V., Amsterdam, 4779–4874. Heckman, J. J. (1979). Sample selection bias as a...Reflections on Heckman and Pinto’s “Causal Analysis After Haavelmo” Judea Pearl University of California, Los Angeles Computer Science Department Los

Energy modeling. Volume 2: Inventory and details of state energy models

NASA Astrophysics Data System (ADS)

Melcher, A. G.; Underwood, R. G.; Weber, J. C.; Gist, R. L.; Holman, R. P.; Donald, D. W.

1981-05-01

An inventory of energy models developed by or for state governments is presented, and certain models are discussed in depth. These models address a variety of purposes such as: supply or demand of energy or of certain types of energy; emergency management of energy; and energy economics. Ten models are described. The purpose, use, and history of the model is discussed, and information is given on the outputs, inputs, and mathematical structure of the model. The models include five models dealing with energy demand, one of which is econometric and four of which are econometric-engineering end-use models.

The Changing Balance: South and North Korean Capabilities for Long-Term Military Competition

DTIC Science & Technology

1985-12-01

econometric model. Ideally, a model should be estimated over one period and then tested over a different period. If one esti- mates and tests over the...unprecedented impending shift of political leadership from Kim II-Sung to his son, Kim Chong-Il. Section III summarizes an aggregative econometric ...model of the South Korean economy, which we have developed to test the effect on that economy of alternative South Korean military force postures and

A spatial econometric analysis of land-use change with land cover trends data: an application to the Pacific Northwest

Treesearch

David J. Lewis; Ralph J. Alig

2014-01-01

This paper develops a plot-level spatial econometric land-use model and estimates it with U.S. Geological Survey Land Cover Trends (LCT) geographic information system panel data for the western halves of the states of Oregon and Washington. The discrete-choice framework we use models plot-scale choices of the three dominant land uses in this region: forest, agriculture...

Crime Pattern Analysis: A Spatial Frequent Pattern Mining Approach

DTIC Science & Technology

2012-05-10

econometrics. A companion to theoretical econometrics, pages 310-330, 1988. [5] L. Anselin, J. Cohen, D. Cook, W. Gorr, and G. Tita . Spatial analyses...52] G. Mohler, M. Short, P. Brantingham, F. Schoenberg, and G. Tita . Self-exciting point process modeling of crime. Journal of the American...Systems, 9:462, 2010. [69] M. Short, P. Brantingham, A. Bertozzi, and G. Tita . Dissipation and displacement of hotspots in reaction-diffusion models

Trading Off Global Fuel Supply, CO2 Emissions and Sustainable Development.

PubMed

Wagner, Liam; Ross, Ian; Foster, John; Hankamer, Ben

2016-01-01

The United Nations Conference on Climate Change (Paris 2015) reached an international agreement to keep the rise in global average temperature 'well below 2°C' and to 'aim to limit the increase to 1.5°C'. These reductions will have to be made in the face of rising global energy demand. Here a thoroughly validated dynamic econometric model (Eq 1) is used to forecast global energy demand growth (International Energy Agency and BP), which is driven by an increase of the global population (UN), energy use per person and real GDP (World Bank and Maddison). Even relatively conservative assumptions put a severe upward pressure on forecast global energy demand and highlight three areas of concern. First, is the potential for an exponential increase of fossil fuel consumption, if renewable energy systems are not rapidly scaled up. Second, implementation of internationally mandated CO2 emission controls are forecast to place serious constraints on fossil fuel use from ~2030 onward, raising energy security implications. Third is the challenge of maintaining the international 'pro-growth' strategy being used to meet poverty alleviation targets, while reducing CO2 emissions. Our findings place global economists and environmentalists on the same side as they indicate that the scale up of CO2 neutral renewable energy systems is not only important to protect against climate change, but to enhance global energy security by reducing our dependence of fossil fuels and to provide a sustainable basis for economic development and poverty alleviation. Very hard choices will have to be made to achieve 'sustainable development' goals.

Trading Off Global Fuel Supply, CO2 Emissions and Sustainable Development

PubMed Central

Wagner, Liam; Ross, Ian; Foster, John; Hankamer, Ben

2016-01-01

The United Nations Conference on Climate Change (Paris 2015) reached an international agreement to keep the rise in global average temperature ‘well below 2°C’ and to ‘aim to limit the increase to 1.5°C’. These reductions will have to be made in the face of rising global energy demand. Here a thoroughly validated dynamic econometric model (Eq 1) is used to forecast global energy demand growth (International Energy Agency and BP), which is driven by an increase of the global population (UN), energy use per person and real GDP (World Bank and Maddison). Even relatively conservative assumptions put a severe upward pressure on forecast global energy demand and highlight three areas of concern. First, is the potential for an exponential increase of fossil fuel consumption, if renewable energy systems are not rapidly scaled up. Second, implementation of internationally mandated CO2 emission controls are forecast to place serious constraints on fossil fuel use from ~2030 onward, raising energy security implications. Third is the challenge of maintaining the international ‘pro-growth’ strategy being used to meet poverty alleviation targets, while reducing CO2 emissions. Our findings place global economists and environmentalists on the same side as they indicate that the scale up of CO2 neutral renewable energy systems is not only important to protect against climate change, but to enhance global energy security by reducing our dependence of fossil fuels and to provide a sustainable basis for economic development and poverty alleviation. Very hard choices will have to be made to achieve ‘sustainable development’ goals. PMID:26959977

Three essays on price dynamics and causations among energy markets and macroeconomic information

NASA Astrophysics Data System (ADS)

Hong, Sung Wook

This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period.

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

NASA Astrophysics Data System (ADS)

Tsutsumi, Morito; Seya, Hajime

2009-12-01

This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.

Crystal study and econometric model

NASA Technical Reports Server (NTRS)

1975-01-01

An econometric model was developed that can be used to predict demand and supply figures for crystals over a time horizon roughly concurrent with that of NASA's Space Shuttle Program - that is, 1975 through 1990. The model includes an equation to predict the impact on investment in the crystal-growing industry. Actually, two models are presented. The first is a theoretical model which follows rather strictly the standard theoretical economic concepts involved in supply and demand analysis, and a modified version of the model was developed which, though not quite as theoretically sound, was testable utilizing existing data sources.

Time Series Modeling of Army Mission Command Communication Networks: An Event-Driven Analysis

DTIC Science & Technology

2013-06-01

Lehmann, D. R. (1984). How advertising affects sales: Meta- analysis of econometric results. Journal of Marketing Research , 21, 65-74. Barabási, A. L...317-357. Leone, R. P. (1983). Modeling sales-advertising relationships: An integrated time series- econometric approach. Journal of Marketing ... Research , 20, 291-295. McGrath, J. E., & Kravitz, D. A. (1982). Group research. Annual Review of Psychology, 33, 195- 230. Monge, P. R., & Contractor

The analysis of factors of management of safety of critical information infrastructure with use of dynamic models

NASA Astrophysics Data System (ADS)

Trostyansky, S. N.; Kalach, A. V.; Lavlinsky, V. V.; Lankin, O. V.

2018-03-01

Based on the analysis of the dynamic model of panel data by region, including fire statistics for surveillance sites and statistics of a set of regional socio-economic indicators, as well as the time of rapid response of the state fire service to fires, the probability of fires in the surveillance sites and the risk of human death in The result of such fires from the values of the corresponding indicators for the previous year, a set of regional social-economics factors, as well as regional indicators time rapid response of the state fire service in the fire. The results obtained are consistent with the results of the application to the fire risks of the model of a rational offender. Estimation of the economic equivalent of human life from data on surveillance objects for Russia, calculated on the basis of the analysis of the presented dynamic model of fire risks, correctly agrees with the known literary data. The results obtained on the basis of the econometric approach to fire risks allow us to forecast fire risks at the supervisory sites in the regions of Russia and to develop management solutions to minimize such risks.

An Annotated Bibliography on Techniques of Forecasting Demand for Water.

DTIC Science & Technology

1981-05-01

power of macroeconomic theory. There is hope that new computel capabilities and econometric techniques will siqnificantly improve in tie near future...umptioii r 1 at ion -Ii frs r r c (I I~ t ia 1 wa t or - uiso pro oa ((,t I onT.s,-! use( no r uemp loyee i t 1n ,o cif -nmfercia 1 consumpt ion. Aer i...ou t plt i ai cr ivo frrn it iitt At-coitLi t abl( for t it, NAP. A deat ail ad ed ri pt iokn of th, mod’ I , nd da ta rtcgu 1 rmn iL in hf, found it

Econometric Methods for Research in Education. NBER Working Paper No. 16003

ERIC Educational Resources Information Center

Meghir, Costas; Rivkin, Steven G.

2010-01-01

This paper reviews some of the econometric methods that have been used in the economics of education. The focus is on understanding how the assumptions made to justify and implement such methods relate to the underlying economic model and the interpretation of the results. We start by considering the estimation of the returns to education both…

Econometric Estimation of the Economic Impact of a University. AIR 1993 Annual Forum Paper.

ERIC Educational Resources Information Center

Gana, Rajaram

This study conducted an econometric analysis of the impact of the University of Delaware (UD), a public, doctoral level institution, on the Delaware economy, particularly the impact of nonresident students. To construct a model the study used historical institutional data from the Office of Institutional Research and Planning at UD and…

Determinants of Educational Achievement in Morocco: A Micro-Econometric Analysis Applied to the TIMSS Study

ERIC Educational Resources Information Center

Ibourk, Aomar

2013-01-01

Based on data from international surveys measuring learning (TIMSS), this article focuses on the analysis of the academic performance Moroccan students. The results of the econometric model show that the students' characteristics, their family environment and school context are key determinants of these performances. The study also shows that the…

An Initial Econometric Consideration of Supply and Demand in the Guaranteed Student Loan Program.

ERIC Educational Resources Information Center

Bayus, Barry; Kendis, Kurt

1982-01-01

In this econometric model of the Guaranteed Student Loan Program (GSLP), supply is related to banks' liquidity and yield curves, all lenders' economic costs and returns, and Student Loan Marketing Association activity. GSLP demand is based on loan costs, family debt position, and net student need for financial aid. (RW)

Computational Models of Consumer Confidence from Large-Scale Online Attention Data: Crowd-Sourcing Econometrics

PubMed Central

2015-01-01

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting. PMID:25826692

Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics.

PubMed

Dong, Xianlei; Bollen, Johan

2015-01-01

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

Econometric model for age- and population-dependent radiation exposures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sandquist, G.M.; Slaughter, D.M.; Rogers, V.C.

1991-01-01

The economic impact associated with ionizing radiation exposures in a given human population depends on numerous factors including the individual's mean economic status as a function age, the age distribution of the population, the future life expectancy at each age, and the latency period for the occurrence of radiation-induced health effects. A simple mathematical model has been developed that provides an analytical methodology for estimating the societal econometrics associated with radiation effects are to be assessed and compared for economic evaluation.

Patterns of Marine Corps Reserve Continuation Behavior: Pre- and Post-9/11

DTIC Science & Technology

2011-03-01

to consider when studying reserve retention and very difficult to measure using multivariate econometric models, which rely solely on observational...chapter present an interesting supplement to standard economic theoretical perspectives commonly used in econometric analyses. Notably, the structural...relevant to this thesis. These factors contribute to the over- arching themes of job satisfaction and organizational commitment and therefore ultimately

Econometric analysis of the factors influencing forest acreage trends in the southeast.

Treesearch

Ralph J. Alig

1986-01-01

Econometric models of changes in land use acreages in the Southeast by physiographic region have been developed by pooling cross-section and time series data. Separate acreage equations have been estimated for the three major private forestland owner classes and the three major classes of nonforest land use. Observations were drawn at three or four different points in...

Day of the week effect in paper submission/acceptance/rejection to/in/by peer review journals. II. An ARCH econometric-like modeling

NASA Astrophysics Data System (ADS)

Ausloos, Marcel; Nedic, Olgica; Dekanski, Aleksandar; Mrowinski, Maciej J.; Fronczak, Piotr; Fronczak, Agata

2017-02-01

This paper aims at providing a statistical model for the preferred behavior of authors submitting a paper to a scientific journal. The electronic submission of (about 600) papers to the Journal of the Serbian Chemical Society has been recorded for every day from Jan. 01, 2013 till Dec. 31, 2014, together with the acceptance or rejection paper fate. Seasonal effects and editor roles (through desk rejection and subfield editors) are examined. An ARCH-like econometric model is derived stressing the main determinants of the favorite day-of-week process.

Billions of Dollars are Involved in Taxation of the Life Insurance Industry -- Some Corrections in the Law are Needed.

DTIC Science & Technology

1981-09-17

leading life companies, 1979 69 i A A TABLES 16 A comparative example of the reserve test Calculation 76 17 Comparative income tax burden of life...pp. 159-61. 2/J. David Cummins, An Econometric Model of the Life Insurance Sector of the U.S. Economy (Lexington, Mass.: Lexington Books, 1975), p. 57...3/Cummins, Econometric Model, p. 44. 4/Fact Book 1979, p. 32. 23 decades earlier. 1/ This decline has been attributed to two sources. First, as

Econometrics and data of the 9 sector Dynamic General Equilibrium Model. Volume III. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berndt, E.R.; Fraumeni, B.M.; Hudson, E.A.

1981-03-01

This report presents the econometrics and data of the 9 sector Dynamic General Equilibrium Model. There are two key components of 9DGEM - the model of household behavior and the model of produconcrneer behavior. The household model is concerned with decisions on consumption, saving, labor supply and the composition of consumption. The producer model is concerned with output price formation and determination of input patterns and purchases for each of the nine producing sectors. These components form the behavioral basis of DGEM. The remaining components are concerned with constraints, balance conditions, accounting, and government revenues and expenditures (these elements aremore » developed in the report on the model specification).« less

Data cleaning in the energy domain

NASA Astrophysics Data System (ADS)

Akouemo Kengmo Kenfack, Hermine N.

This dissertation addresses the problem of data cleaning in the energy domain, especially for natural gas and electric time series. The detection and imputation of anomalies improves the performance of forecasting models necessary to lower purchasing and storage costs for utilities and plan for peak energy loads or distribution shortages. There are various types of anomalies, each induced by diverse causes and sources depending on the field of study. The definition of false positives also depends on the context. The analysis is focused on energy data because of the availability of data and information to make a theoretical and practical contribution to the field. A probabilistic approach based on hypothesis testing is developed to decide if a data point is anomalous based on the level of significance. Furthermore, the probabilistic approach is combined with statistical regression models to handle time series data. Domain knowledge of energy data and the survey of causes and sources of anomalies in energy are incorporated into the data cleaning algorithm to improve the accuracy of the results. The data cleaning method is evaluated on simulated data sets in which anomalies were artificially inserted and on natural gas and electric data sets. In the simulation study, the performance of the method is evaluated for both detection and imputation on all identified causes of anomalies in energy data. The testing on utilities' data evaluates the percentage of improvement brought to forecasting accuracy by data cleaning. A cross-validation study of the results is also performed to demonstrate the performance of the data cleaning algorithm on smaller data sets and to calculate an interval of confidence for the results. The data cleaning algorithm is able to successfully identify energy time series anomalies. The replacement of those anomalies provides improvement to forecasting models accuracy. The process is automatic, which is important because many data cleaning processes require human input and become impractical for very large data sets. The techniques are also applicable to other fields such as econometrics and finance, but the exogenous factors of the time series data need to be well defined.

Econometrically calibrated computable general equilibrium models: Applications to the analysis of energy and climate politics

NASA Astrophysics Data System (ADS)

Schu, Kathryn L.

Economy-energy-environment models are the mainstay of economic assessments of policies to reduce carbon dioxide (CO2) emissions, yet their empirical basis is often criticized as being weak. This thesis addresses these limitations by constructing econometrically calibrated models in two policy areas. The first is a 35-sector computable general equilibrium (CGE) model of the U.S. economy which analyzes the uncertain impacts of CO2 emission abatement. Econometric modeling of sectors' nested constant elasticity of substitution (CES) cost functions based on a 45-year price-quantity dataset yields estimates of capital-labor-energy-material input substitution elasticities and biases of technical change that are incorporated into the CGE model. I use the estimated standard errors and variance-covariance matrices to construct the joint distribution of the parameters of the economy's supply side, which I sample to perform Monte Carlo baseline and counterfactual runs of the model. The resulting probabilistic abatement cost estimates highlight the importance of the uncertainty in baseline emissions growth. The second model is an equilibrium simulation of the market for new vehicles which I use to assess the response of vehicle prices, sales and mileage to CO2 taxes and increased corporate average fuel economy (CAFE) standards. I specify an econometric model of a representative consumer's vehicle preferences using a nested CES expenditure function which incorporates mileage and other characteristics in addition to prices, and develop a novel calibration algorithm to link this structure to vehicle model supplies by manufacturers engaged in Bertrand competition. CO2 taxes' effects on gasoline prices reduce vehicle sales and manufacturers' profits if vehicles' mileage is fixed, but these losses shrink once mileage can be adjusted. Accelerated CAFE standards induce manufacturers to pay fines for noncompliance rather than incur the higher costs of radical mileage improvements. Neither policy induces major increases in fuel economy.

Econometric models for predicting confusion crop ratios

NASA Technical Reports Server (NTRS)

Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

1979-01-01

Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

Disaggregating residential water demand for improved forecasts and decision making

NASA Astrophysics Data System (ADS)

Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

2003-04-01

Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water-using fixtures, and ranges from hedonism (installing pools and whirlpool tubs) to satisficing (adjusting irrigation timers only twice per year) to acting on deeply-held conservation ethics in ways that not only fail any benefit-cost test, but are discouraged, or even illegal (reuse of gray water and black water). Research findings are being captured in dynamic simulation models that integrate social and natural science to create tools to assist water resource managers in providing sustainable water supplies and improving residential water demand forecasts. These models feature simple, graphical user interfaces and output screens that provide decision makers with visual, easy-to-understand information at the basin level. The models reveal connections between various supply and demand components, and highlight direct impacts and feedback mechanisms associated with various policy options.

Spatial Analysis of China Province-level Perinatal Mortality

PubMed Central

XIANG, Kun; SONG, Deyong

2016-01-01

Background: Using spatial analysis tools to determine the spatial patterns of China province-level perinatal mortality and using spatial econometric model to examine the impacts of health care resources and different socio-economic factors on perinatal mortality. Methods: The Global Moran’s I index is used to examine whether the spatial autocorrelation exists in selected regions and Moran’s I scatter plot to examine the spatial clustering among regions. Spatial econometric models are used to investigate the spatial relationships between perinatal mortality and contributing factors. Results: The overall Moran’s I index indicates that perinatal mortality displays positive spatial autocorrelation. Moran’s I scatter plot analysis implies that there is a significant clustering of mortality in both high-rate regions and low-rate regions. The spatial econometric models analyses confirm the existence of a direct link between perinatal mortality and health care resources, socio-economic factors. Conclusions: Since a positive spatial autocorrelation has been detected in China province-level perinatal mortality, the upgrading of regional economic development and medical service level will affect the mortality not only in region itself but also its adjacent regions. PMID:27398334

Residential demand for energy. Volume 1: Residential energy demand in the US

NASA Astrophysics Data System (ADS)

Taylor, L. D.; Blattenberger, G. R.; Rennhack, R. K.

1982-04-01

Updated and improved versions of the residential energy demand models that are currently used in EPRI's Demand 80/81 Model are presented. The primary objective of the study is the development and estimation of econometric demand models that take into account in a theoretically appropriate way the problems caused by decreasing-block pricing in the sale of electricity and natural gas. An ancillary objective is to take into account the impact on electricity, natural gas, and fuel oil demands of differences and changes in the availability of natural gas. Econometric models of residential demand are estimated for all three fuel tyes using time series data by state. Price and income elasticities for a number of alternative models are presented.

Economics of technological change - A joint model for the aircraft and airline industries

NASA Technical Reports Server (NTRS)

Kneafsey, J. T.; Taneja, N. K.

1981-01-01

The principal focus of this econometric model is on the process of technological change in the U.S. aircraft manufacturing and airline industries. The problem of predicting the rate of introduction of current technology aircraft into an airline's fleet during the period of research, development, and construction for new technology aircraft arises in planning aeronautical research investments. The approach in this model is a statistical one. It attempts to identify major factors that influence transport aircraft manufacturers and airlines, and to correlate them with the patterns of delivery of new aircraft to the domestic trunk carriers. The functional form of the model has been derived from several earlier econometric models on the economics of innovation, acquisition, and technological change.

Interplay between past market correlation structure changes and future volatility outbursts.

PubMed

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T

2016-11-18

We report significant relations between past changes in the market correlation structure and future changes in the market volatility. This relation is made evident by using a measure of "correlation structure persistence" on correlation-based information filtering networks that quantifies the rate of change of the market dependence structure. We also measured changes in the correlation structure by means of a "metacorrelation" that measures a lagged correlation between correlation matrices computed over different time windows. Both methods show a deep interplay between past changes in correlation structure and future changes in volatility and we demonstrate they can anticipate market risk variations and this can be used to better forecast portfolio risk. Notably, these methods overcome the curse of dimensionality that limits the applicability of traditional econometric tools to portfolios made of a large number of assets. We report on forecasting performances and statistical significance of both methods for two different equity datasets. We also identify an optimal region of parameters in terms of True Positive and False Positive trade-off, through a ROC curve analysis. We find that this forecasting method is robust and it outperforms logistic regression predictors based on past volatility only. Moreover the temporal analysis indicates that methods based on correlation structural persistence are able to adapt to abrupt changes in the market, such as financial crises, more rapidly than methods based on past volatility.

Interplay between past market correlation structure changes and future volatility outbursts

NASA Astrophysics Data System (ADS)

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T.

2016-11-01

We report significant relations between past changes in the market correlation structure and future changes in the market volatility. This relation is made evident by using a measure of “correlation structure persistence” on correlation-based information filtering networks that quantifies the rate of change of the market dependence structure. We also measured changes in the correlation structure by means of a “metacorrelation” that measures a lagged correlation between correlation matrices computed over different time windows. Both methods show a deep interplay between past changes in correlation structure and future changes in volatility and we demonstrate they can anticipate market risk variations and this can be used to better forecast portfolio risk. Notably, these methods overcome the curse of dimensionality that limits the applicability of traditional econometric tools to portfolios made of a large number of assets. We report on forecasting performances and statistical significance of both methods for two different equity datasets. We also identify an optimal region of parameters in terms of True Positive and False Positive trade-off, through a ROC curve analysis. We find that this forecasting method is robust and it outperforms logistic regression predictors based on past volatility only. Moreover the temporal analysis indicates that methods based on correlation structural persistence are able to adapt to abrupt changes in the market, such as financial crises, more rapidly than methods based on past volatility.

Interplay between past market correlation structure changes and future volatility outbursts

PubMed Central

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T.

2016-01-01

We report significant relations between past changes in the market correlation structure and future changes in the market volatility. This relation is made evident by using a measure of “correlation structure persistence” on correlation-based information filtering networks that quantifies the rate of change of the market dependence structure. We also measured changes in the correlation structure by means of a “metacorrelation” that measures a lagged correlation between correlation matrices computed over different time windows. Both methods show a deep interplay between past changes in correlation structure and future changes in volatility and we demonstrate they can anticipate market risk variations and this can be used to better forecast portfolio risk. Notably, these methods overcome the curse of dimensionality that limits the applicability of traditional econometric tools to portfolios made of a large number of assets. We report on forecasting performances and statistical significance of both methods for two different equity datasets. We also identify an optimal region of parameters in terms of True Positive and False Positive trade-off, through a ROC curve analysis. We find that this forecasting method is robust and it outperforms logistic regression predictors based on past volatility only. Moreover the temporal analysis indicates that methods based on correlation structural persistence are able to adapt to abrupt changes in the market, such as financial crises, more rapidly than methods based on past volatility. PMID:27857144

Much ado about two: reconsidering retransformation and the two-part model in health econometrics.

PubMed

Mullahy, J

1998-06-01

In health economics applications involving outcomes (y) and covariates (x), it is often the case that the central inferential problems of interest involve E[y/x] and its associated partial effects or elasticities. Many such outcomes have two fundamental statistical properties: y > or = 0; and the outcome y = 0 is observed with sufficient frequency that the zeros cannot be ignored econometrically. This paper (1) describes circumstances where the standard two-part model with homoskedastic retransformation will fail to provide consistent inferences about important policy parameters; and (2) demonstrates some alternative approaches that are likely to prove helpful in applications.

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haupt, Sue Ellen

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solarmore » power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.« less

An econometric investigation of the sunspot number record since the year 1700 and its prediction into the 22nd century

NASA Astrophysics Data System (ADS)

Travaglini, Guido

2015-09-01

Solar activity, as measured by the yearly revisited time series of sunspot numbers (SSN) for the period 1700-2014 (Clette et al., 2014), undergoes in this paper a triple statistical and econometric checkup. The conclusions are that the SSN sequence: (1) is best modeled as a signal that features nonlinearity in mean and variance, long memory, mean reversion, 'threshold' symmetry, and stationarity; (2) is best described as a discrete damped harmonic oscillator which linearly approximates the flux-transport dynamo model; (3) its prediction well into the 22nd century testifies of a substantial fall of the SSN centered around the year 2030. In addition, the first and last Gleissberg cycles show almost the same peak number and height during the period considered, yet the former slightly prevails when measured by means of the estimated smoother. All of these conclusions are achieved by making use of modern tools developed in the field of Financial Econometrics and of two new proposed procedures for signal smoothing and prediction.

Scale Mixture Models with Applications to Bayesian Inference

NASA Astrophysics Data System (ADS)

Qin, Zhaohui S.; Damien, Paul; Walker, Stephen

2003-11-01

Scale mixtures of uniform distributions are used to model non-normal data in time series and econometrics in a Bayesian framework. Heteroscedastic and skewed data models are also tackled using scale mixture of uniform distributions.

Perspectives on econometric modelling to inform policy: a UK qualitative case study of minimum unit pricing of alcohol

PubMed Central

Bond, Lyndal; Hilton, Shona

2014-01-01

Background: Novel policy interventions may lack evaluation-based evidence. Considerations to introduce minimum unit pricing (MUP) of alcohol in the UK were informed by econometric modelling (the ‘Sheffield model’). We aim to investigate policy stakeholders’ views of the utility of modelling studies for public health policy. Methods: In-depth qualitative interviews with 36 individuals involved in MUP policy debates (purposively sampled to include civil servants, politicians, academics, advocates and industry-related actors) were conducted and thematically analysed. Results: Interviewees felt familiar with modelling studies and often displayed detailed understandings of the Sheffield model. Despite this, many were uneasy about the extent to which the Sheffield model could be relied on for informing policymaking and preferred traditional evaluations. A tension was identified between this preference for post hoc evaluations and a desire for evidence derived from local data, with modelling seen to offer high external validity. MUP critics expressed concern that the Sheffield model did not adequately capture the ‘real life’ world of the alcohol market, which was conceptualized as a complex and, to some extent, inherently unpredictable system. Communication of modelling results was considered intrinsically difficult but presenting an appropriate picture of the uncertainties inherent in modelling was viewed as desirable. There was general enthusiasm for increased use of econometric modelling to inform future policymaking but an appreciation that such evidence should only form one input into the process. Conclusion: Modelling studies are valued by policymakers as they provide contextually relevant evidence for novel policies, but tensions exist with views of traditional evaluation-based evidence. PMID:24367068

Econometrics of exhaustible resource supply: a theory and an application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Epple, D.

1983-01-01

This report takes a major step toward developing a fruitful approach to empirical analysis of resource supply. It is the first empirical application of resource theory that has successfully integrated the effects of depletion of nonrenewable resources with the effects of uncertainty about future costs and prices on supply behavior. Thus, the model is a major improvement over traditional engineering-optimization models that assume complete certainty, and over traditional econometrics models that are only implicitly related to the theory of resource supply. The model is used to test hypotheses about interdependence of oil and natural gas discoveries, depletion, ultimate recovery, andmore » the role of price expectations. This paper demonstrates the feasibility of using exhaustible resource theory in the development of empirically testable models. 19 refs., 1 fig., 5 tabs.« less

Informing Ex Ante Event Studies with Macro-Econometric Evidence on the Structural and Policy Impacts of Terrorism.

PubMed

Nassios, Jason; Giesecke, James A

2018-04-01

Economic consequence analysis is one of many inputs to terrorism contingency planning. Computable general equilibrium (CGE) models are being used more frequently in these analyses, in part because of their capacity to accommodate high levels of event-specific detail. In modeling the potential economic effects of a hypothetical terrorist event, two broad sets of shocks are required: (1) physical impacts on observable variables (e.g., asset damage); (2) behavioral impacts on unobservable variables (e.g., investor uncertainty). Assembling shocks describing the physical impacts of a terrorist incident is relatively straightforward, since estimates are either readily available or plausibly inferred. However, assembling shocks describing behavioral impacts is more difficult. Values for behavioral variables (e.g., required rates of return) are typically inferred or estimated by indirect means. Generally, this has been achieved via reference to extraneous literature or ex ante surveys. This article explores a new method. We elucidate the magnitude of CGE-relevant structural shifts implicit in econometric evidence on terrorist incidents, with a view to informing future ex ante event assessments. Ex post econometric studies of terrorism by Blomberg et al. yield macro econometric equations that describe the response of observable economic variables (e.g., GDP growth) to terrorist incidents. We use these equations to determine estimates for relevant (unobservable) structural and policy variables impacted by terrorist incidents, using a CGE model of the United States. This allows us to: (i) compare values for these shifts with input assumptions in earlier ex ante CGE studies; and (ii) discuss how future ex ante studies can be informed by our analysis. © 2017 Society for Risk Analysis.

Choosing the right amount of healthcare information technologies investments.

PubMed

Meyer, Rodolphe; Degoulet, Patrice

2010-04-01

Choosing and justifying the right amount of investment in healthcare information technologies (HITECH or HIT) in hospitals is an ever increasing challenge. Our objectives are to assess the financial impact of HIT on hospital outcome, and propose decision-helping tools that could be used to rationalize the distribution of hospital finances. We used a production function and microeconomic tools on data of 21 Paris university hospitals recorded from 1998 to 2006 to compute the elasticity coefficients of HIT versus non-HIT capital and labor as regards to hospital financial outcome and optimize the distribution of investments according to the productivity associated with each input. HIT inputs and non-HIT inputs both have a positive and significant impact on hospital production (elasticity coefficients respectively of 0.106 and 0.893; R(2) of 0.92). We forecast 2006 results from the 1998 to 2005 dataset with an accuracy of +0.61%. With the model used, the best proportion of HIT investments was estimated to be 10.6% of total input and this was predicted to lead to a total saving of 388 million Euros for the 2006 dataset. Considering HIT investment from the point of view of a global portfolio and applying econometric and microeconomic tools allow the required confidence level to be attained for choosing the right amount of HIT investments. It could also allow hospitals using these tools to make substantial savings, and help them forecast their choices for the following year for better HITECH governance in the current stimulation context. (c) 2010 Elsevier Ireland Ltd. All rights reserved.

Irrigation water demand: A meta-analysis of price elasticities

NASA Astrophysics Data System (ADS)

Scheierling, Susanne M.; Loomis, John B.; Young, Robert A.

2006-01-01

Metaregression models are estimated to investigate sources of variation in empirical estimates of the price elasticity of irrigation water demand. Elasticity estimates are drawn from 24 studies reported in the United States since 1963, including mathematical programming, field experiments, and econometric studies. The mean price elasticity is 0.48. Long-run elasticities, those that are most useful for policy purposes, are likely larger than the mean estimate. Empirical results suggest that estimates may be more elastic if they are derived from mathematical programming or econometric studies and calculated at a higher irrigation water price. Less elastic estimates are found to be derived from models based on field experiments and in the presence of high-valued crops.

Integrating geographic information systems and remote sensing with spatial econometric and mixed logit models for environmental valuation

NASA Astrophysics Data System (ADS)

Wells, Aaron Raymond

This research focuses on the Emory and Obed Watersheds in the Cumberland Plateau in Central Tennessee and the Lower Hatchie River Watershed in West Tennessee. A framework based on market and nonmarket valuation techniques was used to empirically estimate economic values for environmental amenities and negative externalities in these areas. The specific techniques employed include a variation of hedonic pricing and discrete choice conjoint analysis (i.e., choice modeling), in addition to geographic information systems (GIS) and remote sensing. Microeconomic models of agent behavior, including random utility theory and profit maximization, provide the principal theoretical foundation linking valuation techniques and econometric models. The generalized method of moments estimator for a first-order spatial autoregressive function and mixed logit models are the principal econometric methods applied within the framework. The dissertation is subdivided into three separate chapters written in a manuscript format. The first chapter provides the necessary theoretical and mathematical conditions that must be satisfied in order for a forest amenity enhancement program to be implemented. These conditions include utility, value, and profit maximization. The second chapter evaluates the effect of forest land cover and information about future land use change on respondent preferences and willingness to pay for alternative hypothetical forest amenity enhancement options. Land use change information and the amount of forest land cover significantly influenced respondent preferences, choices, and stated willingness to pay. Hicksian welfare estimates for proposed enhancement options ranged from 57.42 to 25.53, depending on the policy specification, information level, and econometric model. The third chapter presents economic values for negative externalities associated with channelization that affect the productivity and overall market value of forested wetlands. Results of robust, generalized moments estimation of a double logarithmic first-order spatial autoregressive error model (inverse distance weights with spatial dependence up to 1500m) indicate that the implicit cost of damages to forested wetlands caused by channelization equaled -$5,438 ha-1. Collectively, the results of this dissertation provide economic measures of the damages to and benefits of environmental assets, help private landowners and policy makers identify the amenity attributes preferred by the public, and improve the management of natural resources.

Empirical spatial econometric modelling of small scale neighbourhood

NASA Astrophysics Data System (ADS)

Gerkman, Linda

2012-07-01

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

Intrafirm planning and mathematical modeling of owner's equity in industrial enterprises

NASA Astrophysics Data System (ADS)

Ponomareva, S. V.; Zheleznova, I. V.

2018-05-01

The article aims to review the different approaches to intrafirm planning of owner's equity in industrial enterprises. Since charter capital, additional capital and reserve capital do not change in the process of enterprise activity, the main interest lies on the field of share repurchases from shareholders and retained earnings within the owner's equity of the enterprise. In order to study the effect of share repurchases on the activities of the enterprise, let us use such mathematical methods as event study and econometric modeling. This article describes the step-by-step algorithm of carrying out event study and justifies the choice of Logit model in econometric analysis. The article represents basic results of conducted regression analysis on the effect of share repurchases on the key financial indicators in industrial enterprises.

Long-term energy security in a national scale using LEAP. Application to de-carbonization scenarios in Andorra

NASA Astrophysics Data System (ADS)

Travesset-Baro, Oriol; Jover, Eric; Rosas-Casals, Marti

2016-04-01

This paper analyses the long-term energy security in a national scale using Long-range Energy Alternatives Planning System (LEAP) modelling tool. It builds the LEAP Andorra model, which forecasts energy demand and supply for the Principality of Andorra by 2050. It has a general bottom-up structure, where energy demand is driven by the technological composition of the sectors of the economy. The technological model is combined with a top-down econometric model to take into account macroeconomic trends. The model presented in this paper provides an initial estimate of energy demand in Andorra segregated into all sectors (residential, transport, secondary, tertiary and public administration) and charts a baseline scenario based on historical trends. Additional scenarios representing different policy strategies are built to explore the country's potential energy savings and the feasibility to achieve the Intended Nationally Determined Contribution (INDC) submitted in April 2015 to UN. In this climatic agreement Andorra intends to reduce net greenhouse gas emissions (GHG) by 37% as compared to a business-as-usual scenario by 2030. In addition, current and future energy security is analysed in this paper under baseline and de-carbonization scenarios. Energy security issues are assessed in LEAP with an integrated vision, going beyond the classic perspective of security of supply, and being closer to the sustainability's integrative vision. Results of scenarios show the benefits of climate policies in terms of national energy security and the difficulties for Andorra to achieving the de-carbonization target by 2030.

Bootstrapping Student Understanding of What Is Going on in Econometrics.

ERIC Educational Resources Information Center

Kennedy, Peter E.

2001-01-01

Explains that econometrics is an intellectual game played by rules based on the sampling distribution concept. Contains explanations for why many students are uncomfortable with econometrics. Encourages instructors to use explain-how-to-bootstrap exercises to promote student understanding. (RLH)

Estimating Environmental Compliance Costs for Industry (1981)

EPA Pesticide Factsheets

The paper discusses the pros and cons of existing approaches to compliance cost estimation such as ex post survey estimation and ex ante estimation techniques (input cost accounting methods, engineering process models and, econometric models).

The Science of Science Policy: A Federal Research Roadmap

DTIC Science & Technology

2008-11-01

and Atmospheric Administra on, h p://www.ncdc.noaa.gov/ oa /climate/globalwarming.html#q4. T S S P : A F R R4 maintain the na on’s dominance...econometric studies, surveys, case studies, and retrospec ve analyses. Econometric studies include the macroeconomic growth models pioneered by Robert...R A W ha t a re th e be ha vi or al fo un da o ns o f i nn ov a- o n? U nd er st an di ng th e be ha vi or o f i nd iv id ua ls an d

On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables.

DOT National Transportation Integrated Search

2015-12-01

We develop an econometric framework for incorporating spatial dependence in integrated model systems of latent variables and multidimensional mixed data outcomes. The framework combines Bhats Generalized Heterogeneous Data Model (GHDM) with a spat...

Energy Models and the Policy Process.

ERIC Educational Resources Information Center

De Man, Reinier

1983-01-01

Describes the function of econometric and technological models in the policy process, and shows how different positions in the Dutch energy discussion are reflected by the application of different model methodologies. Discussion includes the energy policy context, a conceptual framework for using energy models, and energy scenarios in policy…

The Assessment of Climatological Impacts on Agricultural Production and Residential Energy Demand

NASA Astrophysics Data System (ADS)

Cooter, Ellen Jean

The assessment of climatological impacts on selected economic activities is presented as a multi-step, inter -disciplinary problem. The assessment process which is addressed explicitly in this report focuses on (1) user identification, (2) direct impact model selection, (3) methodological development, (4) product development and (5) product communication. Two user groups of major economic importance were selected for study; agriculture and gas utilities. The broad agricultural sector is further defined as U.S.A. corn production. The general category of utilities is narrowed to Oklahoma residential gas heating demand. The CERES physiological growth model was selected as the process model for corn production. The statistical analysis for corn production suggests that (1) although this is a statistically complex model, it can yield useful impact information, (2) as a result of output distributional biases, traditional statistical techniques are not adequate analytical tools, (3) the model yield distribution as a whole is probably non-Gausian, particularly in the tails and (4) there appears to be identifiable weekly patterns of forecasted yields throughout the growing season. Agricultural quantities developed include point yield impact estimates and distributional characteristics, geographic corn weather distributions, return period estimates, decision making criteria (confidence limits) and time series of indices. These products were communicated in economic terms through the use of a Bayesian decision example and an econometric model. The NBSLD energy load model was selected to represent residential gas heating consumption. A cursory statistical analysis suggests relationships among weather variables across the Oklahoma study sites. No linear trend in "technology -free" modeled energy demand or input weather variables which would correspond to that contained in observed state -level residential energy use was detected. It is suggested that this trend is largely the result of non-weather factors such as population and home usage patterns rather than regional climate change. Year-to-year changes in modeled residential heating demand on the order of 10('6) Btu's per household were determined and later related to state -level components of the Oklahoma economy. Products developed include the definition of regional forecast areas, likelihood estimates of extreme seasonal conditions and an energy/climate index. This information is communicated in economic terms through an input/output model which is used to estimate changes in Gross State Product and Household income attributable to weather variability.

Adaptability of Irrigation to a Changing Monsoon in India: How far can we go?

NASA Astrophysics Data System (ADS)

Zaveri, E.; Grogan, D. S.; Fisher-Vanden, K.; Frolking, S. E.; Wrenn, D. H.; Nicholas, R.

2014-12-01

Agriculture and the monsoon are inextricably linked in India. A large part of the steady rise in agricultural production since the onset of the Green Revolution in the 1960's has been attributed to irrigation. Irrigation is used to supplement and buffer crops against precipitation shocks, but water availability for such use is itself sensitive to the erratic, seasonal and spatially heterogeneous nature of the monsoon. We provide new evidence on the relationship between monsoon changes, irrigation variability and water availability by linking a process based hydrology model with an econometric model for one of the world's most water stressed countries. India uses more groundwater for irrigation than any other country, and there is substantial evidence that this has led to depletion of groundwater aquifers. First, we build an econometric model of historical irrigation decisions using detailed agriculture and weather data spanning 35 years. Multivariate regression models reveal that for crops grown in the wet season, irrigation is sensitive to distribution and total monsoon rainfall but not to ground or surface water availability. For crops grown in the dry season, total monsoon rainfall matters most, and its effect is sensitive to groundwater availability. The historical estimates from the econometric model are used to calculate future irrigated areas under three different climate model predictions of monsoon climate for the years 2010 - 2050. These projections are then used as input to a physical hydrology model, which quantifies supply of irrigation water from sustainable sources such as rechargeable shallow groundwater, rivers and reservoirs, to unsustainable sources such as non- rechargeable groundwater. We find that the significant variation in monsoon projections lead to very different results. Crops grown in the dry season show particularly divergent trends between model projections, leading to very different groundwater resource requirements.

What is mLearning and How Can It Be Used to Support Learning and Teaching in Econometrics?

ERIC Educational Resources Information Center

Morales, Lucia

2013-01-01

The aim of case this study was to analyze the integration of mobile learning technologies in a postgraduate course in Finance (MSc in Finance) at Dublin Institute of Technology, where econometrics is an important course component. Previous experience with students undertaking econometrics modules supported this analysis, where the researcher…

The Case of Effort Variables in Student Performance.

ERIC Educational Resources Information Center

Borg, Mary O.; And Others

1989-01-01

Tests the existence of a structural shift between above- and below-average students in the econometric models that explain students' grades in principles of economics classes. Identifies a structural shift and estimates separate models for above- and below-average students. Concludes that separate models as well as educational policies are…

Modeling turbidity and flow at daily steps in karst using ARIMA/ARFIMA-GARCH error models

NASA Astrophysics Data System (ADS)

Massei, N.

2013-12-01

Hydrological and physico-chemical variations recorded at karst springs usually reflect highly non-linear processes and the corresponding time series are then very often also highly non-linear. Among others, turbidity, as an important parameter regarding water quality and management, is a very complex response of karst systems to rain events, involving direct transfer of particles from point-source recharge as well as resuspension of particles previously deposited and stored within the system. For those reasons, turbidity modeling has not been well taken in karst hydrological models so far. Most of the time, the modeling approaches would involve stochastic linear models such ARIMA-type models and their derivatives (ARMA, ARMAX, ARIMAX, ARFIMA...). Yet, linear models usually fail to represent well the whole (stochastic) process variability, and their residuals still contain useful information that can be used to either understand the whole variability or to enhance short-term predictability and forecasting. Model residuals are actually not i.i.d., which can be identified by the fact that squared residuals still present clear and significant serial correlation. Indeed, high (low) amplitudes are followed in time by high (low) amplitudes, which can be seen on residuals time series as periods of time during which amplitudes are higher (lower) then the mean amplitude. This is known as the ARCH effet (AutoRegressive Conditional Heteroskedasticity), and the corresponding non-linear process affecting residuals of a linear model can be modeled using ARCH or generalized ARCH (GARCH) non-linear modeling, which approaches are very well known in econometrics. Here we investigated the capability of ARIMA-GARCH error models to represent a ~20-yr daily turbidity time series recorded at a karst spring used for water supply of the city of Le Havre (Upper Normandy, France). ARIMA and ARFIMA models were used to represent the mean behavior of the time series and the residuals clearly appeared to present a pronounced ARCH effect, as confirmed by Ljung-Box and McLeod-Li tests. We then identified and fitted GARCH models to the residuals of ARIMA and ARFIMA models in order to model the conditional variance and volatility of the turbidity time series. The results eventually showed that serial correlation was succesfully removed in the last standardized residuals of the GARCH model, and hence that the ARIMA-GARCH error model appeared consistent for modeling such time series. The approach finally improved short-term (e.g a few steps-ahead) turbidity forecasting.

David Keyser | NREL

Science.gov Websites

. Areas of Expertise Economic impact studies Time series analysis Analysis of labor and demographic data Research Interests Static and dynamic economic impact models Labor data estimation Econometric modeling and 2030: A Strategic Roadmap for American Energy Innovation, Economic Growth, and Competitiveness."

Econometric Models of Education, Some Applications. Education and Development, Technical Reports.

ERIC Educational Resources Information Center

Tinbergen, Jan; And Others

This report contains five papers which describe mathematical models of the educational system as it relates to economic growth. Experimental applications of the models to particular educational systems are discussed. Three papers, by L. J. Emmerij, J. Blum, and G. Williams, discuss planning models for the calculation of educational requirements…

Econometrics as evidence? Examining the 'causal' connections between financial speculation and commodities prices.

PubMed

Williams, James W; Cook, Nikolai M

2016-10-01

One of the lasting legacies of the financial crisis of 2008, and the legislative energies that followed from it, is the growing reliance on econometrics as part of the rulemaking process. Financial regulators are increasingly expected to rationalize proposed rules using available econometric techniques, and the courts have vacated several key rules emanating from Dodd-Frank on the grounds of alleged deficiencies in this evidentiary effort. The turn toward such econometric tools is seen as a significant constraint on and challenge to regulators as they endeavor to engage with such essential policy questions as the impact of financial speculation on food security. Yet, outside of the specialized practitioner community, very little is known about these techniques. This article examines one such econometric test, Granger causality, and its role in a pivotal Dodd-Frank rulemaking. Through an examination of the test for Granger causality and its attempts to distill the causal connections between financial speculation and commodities prices, the article argues that econometrics is a blunt but useful tool, limited in its ability to provide decisive insights into commodities markets and yet yielding useful returns for those who are able to wield it.

Energy risk in the arbitrage pricing model: an empirical and theoretical study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bremer, M.A.

1986-01-01

This dissertation empirically explores the Arbitrage Pricing Theory in the context of energy risk for securities over the 1960s, 1970s, and early 1980s. Starting from a general multifactor pricing model, the paper develops a two factor model based on a market-like factor and an energy factor. This model is then tested on portfolios of securities grouped according to industrial classification using several econometric techniques designed to overcome some of the more serious estimation problems common to these models. The paper concludes that energy risk is priced in the 1970s and possibly even in the 1960s. Energy risk is found tomore » be priced in the sense that investors who hold assets subjected to energy risk are paid for this risk. The classic version of the Capital Asset Pricing Model which posits the market as the single priced factor is rejected in favor of the Arbitrage Pricing Theory or multi-beta versions of the Capital Asset Pricing Model. The study introduces some original econometric methodology to carry out empirical tests.« less

Analytic Methods for Adjusting Subjective Rating Schemes.

ERIC Educational Resources Information Center

Cooper, Richard V. L.; Nelson, Gary R.

Statistical and econometric techniques of correcting for supervisor bias in models of individual performance appraisal were developed, using a variant of the classical linear regression model. Location bias occurs when individual performance is systematically overestimated or underestimated, while scale bias results when raters either exaggerate…

The impact of corruption on the sustainable development of human capital

NASA Astrophysics Data System (ADS)

Absalyamova, Svetlana; Absalyamov, Timur; Khusnullova, Asiya; Mukhametgalieva, Chulpan

2016-08-01

The article explains the use of the human capital sustainable development index (HCSDI) to assess the quality of the reproduction of human capital. The paper provides the algorithm for calculating HCSDI and its components. Authors estimated cross-country differences of HCSDI and developed econometric model of the impact of corruption on HCSDI. The use of this model has allowed to reveal the mechanism and assess the impact of corruption on HCSDI and its components. The results of econometric analysis revealed a negative multiplier effect: an increase in the corruption of the socio-economic system of the state by 1% caused HCSDI reduce by more than 1%. The results and conclusions may be proxy-assessments of the socio-economic consequences of violations of the stability of reproduction of human capital in the conditions of the growth of corruption in the country

Learning-Testing Process in Classroom: An Empirical Simulation Model

ERIC Educational Resources Information Center

Buda, Rodolphe

2009-01-01

This paper presents an empirical micro-simulation model of the teaching and the testing process in the classroom (Programs and sample data are available--the actual names of pupils have been hidden). It is a non-econometric micro-simulation model describing informational behaviors of the pupils, based on the observation of the pupils'…

Modeling and simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hanham, R.; Vogt, W.G.; Mickle, M.H.

1986-01-01

This book presents the papers given at a conference on computerized simulation. Topics considered at the conference included expert systems, modeling in electric power systems, power systems operating strategies, energy analysis, a linear programming approach to optimum load shedding in transmission systems, econometrics, simulation in natural gas engineering, solar energy studies, artificial intelligence, vision systems, hydrology, multiprocessors, and flow models.

Non-robust dynamic inferences from macroeconometric models: Bifurcation stratification of confidence regions

NASA Astrophysics Data System (ADS)

Barnett, William A.; Duzhak, Evgeniya Aleksandrovna

2008-06-01

Grandmont [J.M. Grandmont, On endogenous competitive business cycles, Econometrica 53 (1985) 995-1045] found that the parameter space of the most classical dynamic models is stratified into an infinite number of subsets supporting an infinite number of different kinds of dynamics, from monotonic stability at one extreme to chaos at the other extreme, and with many forms of multiperiodic dynamics in between. The econometric implications of Grandmont’s findings are particularly important, if bifurcation boundaries cross the confidence regions surrounding parameter estimates in policy-relevant models. Stratification of a confidence region into bifurcated subsets seriously damages robustness of dynamical inferences. Recently, interest in policy in some circles has moved to New-Keynesian models. As a result, in this paper we explore bifurcation within the class of New-Keynesian models. We develop the econometric theory needed to locate bifurcation boundaries in log-linearized New-Keynesian models with Taylor policy rules or inflation-targeting policy rules. Central results needed in this research are our theorems on the existence and location of Hopf bifurcation boundaries in each of the cases that we consider.

Income Distribution Over Educational Levels: A Simple Model.

ERIC Educational Resources Information Center

Tinbergen, Jan

An econometric model is formulated that explains income per person in various compartments of the labor market defined by three main levels of education and by education required. The model enables an estimation of the effect of increased access to education on that distribution. The model is based on a production for the economy as a whole; a…

Effect of fare and travel time on the demand for domestic air transportation

NASA Technical Reports Server (NTRS)

Eriksen, S. E.; Liu, E. W.

1979-01-01

An econometric travel demand model was presented. The model was used for analyzing long haul domestic passenger markets in the United States. The results showed the sensitivities of demand to changes in fares and speed reflecting technology through more efficient aircraft designs.

Manpower for the coal mining industry: an assessment of adequacy through the year 2000. Volume II. Technical approach. Final technical report. [USA; forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mendis, M.S.; Rosenberg, J.I.; Medville, D.M.

1980-03-01

This report presents a summary of the analytical approach taken and the conclusions reached in an assessment of the supply and demand for manpower in the coal mining industry through the year 2000. A hybrid system dynamics/econometric model of the coal mining industry was developed which incorporates relationships between technological change, labor productivity, production costs, wages, graduation rates, and other key variables in estimating imbalances between labor supply and demand. Study results indicate that while the supply of production workers is expected to be sufficient under most future demand scenarios, periodic shortages of experienced workers, especially in the Northern Greatmore » Plains can be expected. Other study findings are that the supply of mining engineers will be sufficient under all but the highest coal demand scenario, a shortage of faculty will affect the supply of mining engineers in the near-term and the employment of mining technicians is expected to exhibit the largest increase in any labor category studied. In this volume the nature of the coal mining manpower problem is discussed, a detailed description of that analysis conducted and the sources of data used is provided, and the findings of the study are presented.« less

Predictability of extremes in non-linear hierarchically organized systems

NASA Astrophysics Data System (ADS)

Kossobokov, V. G.; Soloviev, A.

2011-12-01

Understanding the complexity of non-linear dynamics of hierarchically organized systems progresses to new approaches in assessing hazard and risk of the extreme catastrophic events. In particular, a series of interrelated step-by-step studies of seismic process along with its non-stationary though self-organized behaviors, has led already to reproducible intermediate-term middle-range earthquake forecast/prediction technique that has passed control in forward real-time applications during the last two decades. The observed seismic dynamics prior to and after many mega, great, major, and strong earthquakes demonstrate common features of predictability and diverse behavior in course durable phase transitions in complex hierarchical non-linear system of blocks-and-faults of the Earth lithosphere. The confirmed fractal nature of earthquakes and their distribution in space and time implies that many traditional estimations of seismic hazard (from term-less to short-term ones) are usually based on erroneous assumptions of easy tractable analytical models, which leads to widespread practice of their deceptive application. The consequences of underestimation of seismic hazard propagate non-linearly into inflicted underestimation of risk and, eventually, into unexpected societal losses due to earthquakes and associated phenomena (i.e., collapse of buildings, landslides, tsunamis, liquefaction, etc.). The studies aimed at forecast/prediction of extreme events (interpreted as critical transitions) in geophysical and socio-economical systems include: (i) large earthquakes in geophysical systems of the lithosphere blocks-and-faults, (ii) starts and ends of economic recessions, (iii) episodes of a sharp increase in the unemployment rate, (iv) surge of the homicides in socio-economic systems. These studies are based on a heuristic search of phenomena preceding critical transitions and application of methodologies of pattern recognition of infrequent events. Any study of rare phenomena of highly complex origin, by their nature, implies using problem oriented methods, which design breaks the limits of classical statistical or econometric applications. The unambiguously designed forecast/prediction algorithms of the "yes or no" variety, analyze the observable quantitative integrals and indicators available to a given date, then provides unambiguous answer to the question whether a critical transition should be expected or not in the next time interval. Since the predictability of an originating non-linear dynamical system is limited in principle, the probabilistic component of forecast/prediction algorithms is represented by the empirical probabilities of alarms, on one side, and failures-to-predict, on the other, estimated on control sets achieved in the retro- and prospective experiments. Predicting in advance is the only decisive test of forecast/predictions and the relevant on-going experiments are conducted in the case seismic extremes, recessions, and increases of unemployment rate. The results achieved in real-time testing keep being encouraging and confirm predictability of the extremes.

A Note on Verification of Computer Simulation Models

ERIC Educational Resources Information Center

Aigner, Dennis J.

1972-01-01

Establishes an argument that questions the validity of one test'' of goodness-of-fit (the extent to which a series of obtained measures agrees with a series of theoretical measures) for the simulated time path of a simple endogenous (internally developed) variable in a simultaneous, perhaps dynamic econometric model. (Author)

Duration Models to Analyze Dating Relationship: The Controversial Role of Gift Giving.

ERIC Educational Resources Information Center

Huang, Ming-Hui; Yu, Shihti

2000-01-01

Econometric duration models were used to analyze dating relationships of 225 college students. Using gifts to enhance the self, express love, and announce relationships helped ensure the success of relationships. Gifts that were too frequent or rare resulted in self-depreciation and anxiety and harmed relationships. (SK)

Space-time modeling of timber prices

Treesearch

Mo Zhou; Joseph Buongriorno

2006-01-01

A space-time econometric model was developed for pine sawtimber timber prices of 21 geographically contiguous regions in the southern United States. The correlations between prices in neighboring regions helped predict future prices. The impulse response analysis showed that although southern pine sawtimber markets were not globally integrated, local supply and demand...

University-Industry Research Collaboration: A Model to Assess University Capability

ERIC Educational Resources Information Center

Abramo, Giovanni; D'Angelo, Ciriaco Andrea; Di Costa, Flavia

2011-01-01

Scholars and policy makers recognize that collaboration between industry and the public research institutions is a necessity for innovation and national economic development. This work presents an econometric model which expresses the university capability for collaboration with industry as a function of size, location and research quality. The…

Demand for health care in Denmark: results of a national sample survey using contingent valuation.

PubMed

Gyldmark, M; Morrison, G C

2001-10-01

In this paper we use willingness to pay (WTP) to elicit values for private insurance covering treatment for four different health problems. By way of obtaining these values, we test the viability of the contingent valuation method (CVM) and econometric techniques, respectively, as means of eliciting and analysing values from the general public. WTP responses from a Danish national sample survey, which was designed in accordance with existing guidelines, are analysed in terms of consistency and validity checks. Large numbers of zero responses are common in WTP studies, and are found here; therefore, the Heckman selectivity model and log-transformed OLS are employed. The selectivity model is rejected, but test results indicate that the lognormal model yields efficient and unbiased estimates. The results give confidence in the WTP estimates obtained and, more generally, in CVM as a means of valuing publicly provided goods and in econometrics as a tool for analysing WTP results containing many zero responses.

Waste production and regional growth of marine activities an econometric model.

PubMed

Bramati, Maria Caterina

2016-11-15

Coastal regions are characterized by intense human activity and climatic pressures, often intensified by competing interests in the use of marine waters. To assess the effect of public spending on the regional economy, an econometric model is here proposed. Not only are the regional investment and the climatic risks included in the model, but also variables related to the anthropogenic pressure, such as population, economic activities and waste production. Feedback effects of economic and demographic expansion on the pollution of coastal areas are also considered. It is found that dangerous waste increases with growing shipping and transportation activities and with growing population density in non-touristic coastal areas. On the other hand, the amount of non-dangerous wastes increases with marine mining, defense and offshore energy production activities. However, lower waste production occurs in areas where aquaculture and touristic industry are more exploited, and accompanied by increasing regional investment in waste disposal. Copyright © 2016 Elsevier Ltd. All rights reserved.

Public budgets for energy RD&D and the effects on energy intensity and pollution levels.

PubMed

Balsalobre, Daniel; Álvarez, Agustín; Cantos, José María

2015-04-01

This study, based on the N-shaped cubic model of the environmental Kuznets curve, analyzes the evolution of per capita greenhouse gas emissions (GHGpc) using not just economic growth but also public budgets dedicated to energy-oriented research development and demonstration (RD&D) and energy intensity. The empirical evidence, obtained from an econometric model of fixed effects for 28 OECD countries during 1994-2010, suggests that energy innovations help reduce GHGpc levels and mitigate the negative impact of energy intensity on environmental quality. When countries develop active energy RD&D policies, they can reduce both the rates of energy intensity and the level of GHGpc emissions. This paper incorporates a moderating variable to the econometric model that emphasizes the effect that GDP has on energy intensity. It also adds a variable that reflects the difference between countries that have made a greater economic effort in energy RD&D, which in turn corrects the GHG emissions resulting from the energy intensity of each country.

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

Evaluation of statistical models for forecast errors from the HBV model

NASA Astrophysics Data System (ADS)

Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

2010-04-01

SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.

The need for econometric research in laboratory animal operations.

PubMed

Baker, David G; Kearney, Michael T

2015-06-01

The scarcity of research funding can affect animal facilities in various ways. These effects can be evaluated by examining the allocation of financial resources in animal facilities, which can be facilitated by the use of mathematical and statistical methods to analyze economic problems, a discipline known as econometrics. The authors applied econometrics to study whether increasing per diem charges had a negative effect on the number of days of animal care purchased by animal users. They surveyed animal numbers and per diem charges at 20 research institutions and found that demand for large animals decreased as per diem charges increased. The authors discuss some of the challenges involved in their study and encourage research institutions to carry out more robust econometric studies of this and other economic questions facing laboratory animal research.

Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

2017-08-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

Integrating transit and urban form : final report, December 2008.

DOT National Transportation Integrated Search

2008-09-01

This study develops an integrated behavioral model of transit patronage and urban form. Although herein focused on transit, the framework can be easily generalized to study other forms of travel. Advanced econometric methods are used to test specific...

A Systematic Comprehensive Computational Model for Stake Estimation in Mission Assurance: Applying Cyber Security Econometrics System (CSES) to Mission Assurance Analysis Protocol (MAAP)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abercrombie, Robert K; Sheldon, Frederick T; Grimaila, Michael R

2010-01-01

In earlier works, we presented a computational infrastructure that allows an analyst to estimate the security of a system in terms of the loss that each stakeholder stands to sustain as a result of security breakdowns. In this paper, we discuss how this infrastructure can be used in the subject domain of mission assurance as defined as the full life-cycle engineering process to identify and mitigate design, production, test, and field support deficiencies of mission success. We address the opportunity to apply the Cyberspace Security Econometrics System (CSES) to Carnegie Mellon University and Software Engineering Institute s Mission Assurance Analysismore » Protocol (MAAP) in this context.« less

Allocating physicians' overhead costs to services: an econometric/accounting-activity based-approach.

PubMed

Peden, Al; Baker, Judith J

2002-01-01

Using the optimizing properties of econometric analysis, this study analyzes how physician overhead costs (OC) can be allocated to multiple activities to maximize precision in reimbursing the costs of services. Drawing on work by Leibenstein and Friedman, the analysis also shows that allocating OC to multiple activities unbiased by revenue requires controlling for revenue when making the estimates. Further econometric analysis shows that it is possible to save about 10 percent of OC by paying only for those that are necessary.

Quantifying model uncertainty in seasonal Arctic sea-ice forecasts

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, Edward; Barthélemy, Antoine; Chevallier, Matthieu; Cullather, Richard; Fučkar, Neven; Massonnet, François; Posey, Pamela; Wang, Wanqiu; Zhang, Jinlun; Ardilouze, Constantin; Bitz, Cecilia; Vernieres, Guillaume; Wallcraft, Alan; Wang, Muyin

2017-04-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or post-processing techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

Optimizing Tsunami Forecast Model Accuracy

NASA Astrophysics Data System (ADS)

Whitmore, P.; Nyland, D. L.; Huang, P. Y.

2015-12-01

Recent tsunamis provide a means to determine the accuracy that can be expected of real-time tsunami forecast models. Forecast accuracy using two different tsunami forecast models are compared for seven events since 2006 based on both real-time application and optimized, after-the-fact "forecasts". Lessons learned by comparing the forecast accuracy determined during an event to modified applications of the models after-the-fact provide improved methods for real-time forecasting for future events. Variables such as source definition, data assimilation, and model scaling factors are examined to optimize forecast accuracy. Forecast accuracy is also compared for direct forward modeling based on earthquake source parameters versus accuracy obtained by assimilating sea level data into the forecast model. Results show that including assimilated sea level data into the models increases accuracy by approximately 15% for the events examined.

Analytical-numerical solution of a nonlinear integrodifferential equation in econometrics

NASA Astrophysics Data System (ADS)

Kakhktsyan, V. M.; Khachatryan, A. Kh.

2013-07-01

A mixed problem for a nonlinear integrodifferential equation arising in econometrics is considered. An analytical-numerical method is proposed for solving the problem. Some numerical results are presented.

Dreams Deferred? The Relationship between Early and Later Postsecondary Educational Aspirations among Racial/Ethnic Groups

ERIC Educational Resources Information Center

Cooper, Michelle Asha

2009-01-01

This study uses data from the Educational Longitudinal Study of 2002 to test a conceptual model that integrates aspects of sociological and econometric frameworks into a traditional status attainment model for educational aspirations. Using descriptive and logistic analyses, this study advanced understanding of the patterns and stability of…

Revisiting the Principle of Relative Constancy: Consumer Mass Media Expenditures in Belgium.

ERIC Educational Resources Information Center

Dupagne, Michel; Green, R. Jeffery

1996-01-01

Proposes two new econometric models for testing the principle of relative constancy (PRC). Reports on regression and cointegration analyses conducted with Belgian mass media expenditure data from 1953-91. Suggests that alternative mass media expenditure models should be developed because PRC lacks of economic foundation and sound empirical…

Investment in Communications and Transportation: Socio-economic Impacts on Rural Development.

ERIC Educational Resources Information Center

Hilewick, Carol Lee; And Others

Two rural counties served as model areas in a comparison of the size and sequence of socioeconomic changes that investment in communications, as opposed to investment in transportation networks, might stimulate. A series of communications, rail, and highway changes were simulated through the use of an econometric model. An Industrial Communication…

Defense Spending and Regional Growth: An Examination of an Export-Base Model and an Econometric Model.

DTIC Science & Technology

1987-06-01

consumer preferences provide influences that can stimulate the rate of growth of the endogenous and/or exogenous income industries. B. EXPORT INDUSTRIES...location quotient was selected to alleviate 12 some of the problems created by consumer preferences and expendi- ture patterns. This value was compared

Economic impacts of hurricanes on forest owners

Treesearch

Jeffrey P. Prestemon; Thomas P. Holmes

2010-01-01

We present a conceptual model of the economic impacts of hurricanes on timber producers and consumers, offer a framework indicating how welfare impacts can be estimated using econometric estimates of timber price dynamics, and illustrate the advantages of using a welfare theoretic model, which includes (1) welfare estimates that are consistent with neo-classical...

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Environment and air pollution like gun and bullet for low-income countries: war for better health and wealth.

PubMed

Zou, Xiang; Azam, Muhammad; Islam, Talat; Zaman, Khalid

2016-02-01

The objective of the study is to examine the impact of environmental indicators and air pollution on "health" and "wealth" for the low-income countries. The study used a number of promising variables including arable land, fossil fuel energy consumption, population density, and carbon dioxide emissions that simultaneously affect the health (i.e., health expenditures per capita) and wealth (i.e., GDP per capita) of the low-income countries. The general representation for low-income countries has shown by aggregate data that consist of 39 observations from the period of 1975-2013. The study decomposes the data set from different econometric tests for managing robust inferences. The study uses temporal forecasting for the health and wealth model by a vector error correction model (VECM) and an innovation accounting technique. The results show that environment and air pollution is the menace for low-income countries' health and wealth. Among environmental indicators, arable land has the largest variance to affect health and wealth for the next 10-year period, while air pollution exerts the least contribution to change health and wealth of low-income countries. These results indicate the prevalence of war situation, where environment and air pollution become visible like "gun" and "bullet" for low-income countries. There are required sound and effective macroeconomic policies to combat with the environmental evils that affect the health and wealth of the low-income countries.

Potential impact of Thailand's alcohol program on production, consumption, and trade of cassava, sugarcane, and corn

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boonserm, P.

1985-01-01

On the first of May 1980, Thailand's fuel-alcohol program was announced by the Thai government. According to the program, a target of 147 million liters of ethanol would be produced in 1981, from cassava, sugarcane, and other biomasses. Projecting increases in output each year, the target level of ethanol produciton was set at 482 million liters of ethanol for 1986. The proposed amount of ethanol production could create a major shift up in the demand schedule of energy crops such as cassava, sugarcane, and corn. The extent of the adjustments in price, production, consumption, and exports for these energy cropsmore » need to be evaluated. The purpose of this study is to assess the potential impact of Thailand's fuel-alcohol program on price, production, consumption, and exports of three potential energy crops: cassava, sugarcane, and corn. Econometric commodity models of cassava, sugarcane, and corn are constructed and used as a method of assessment. The overall results of the forecasting simulations of the models indicate that the fuel-alcohol program proposed by the Thai government will cause the price, production, and total consumption of cassava, sugarcane, and corn to increase; on the other hand, it will cause exports to decline. In addition, based on the relative prices and the technical coefficients of ethanol production of these three energy crops, this study concludes that only cassava should be used to produce the proposed target of ethanol production.« less

A revised econometric model of the domestic pallet market

Treesearch

Albert T. Schuler; Walter B. Wallin

1983-01-01

The purpose of this revised model is to project estimates of consumption and price of wooden pallets in the short term. This model differs from previous ones developed by Schuler and Wallin (1979 and 1980) in the following respects: The structure of the supply side of the market is more realistically identified (from an economic theory point of view) by including...

Macro policy responses to oil booms and busts in the United Arab Emirates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Al-Mutawa, A.K.

1991-01-01

The effects of oil shocks and macro policy changes in the United Arab Emirates are analyzed. A theoretical model is developed within the framework of the Dutch Disease literature. It contains four unique features that are applicable to the United Arab Emirates' economy. There are: (1) the presence of a large foreign labor force; (2) OPEC's oil export quotas; (3) the division of oil profits; and (4) the important role of government expenditures. The model is then used to examine the welfare effects of the above-mentioned shocks. An econometric model is then specified that conforms to the analytical model. Inmore » the econometric model the method of principal components' is applied owing to the undersized sample data. The principal components methodology is used in both the identification testing and the estimation of the structural equations. The oil and macro policy shocks are then simulated. The simulation results show that an oil-quantity boom leads to a higher welfare gain than an oil-price boom. Under certain circumstances, this finding is also confirmed by the comparative statistics that follow from the analytical model.« less

Bureau of Labor Statistics Employment Projections: Detailed Analysis of Selected Occupations and Industries. Report to the Honorable Berkley Bedell, United States House of Representatives.

ERIC Educational Resources Information Center

General Accounting Office, Washington, DC.

To compile its projections of future employment levels, the Bureau of Labor Statistics (BLS) combines the following five interlinked models in a six-step process: a labor force model, an econometric model of the U.S. economy, an industry activity model, an industry labor demand model, and an occupational labor demand model. The BLS was asked to…

Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions

NASA Astrophysics Data System (ADS)

Medina, Hanoi; Tian, Di; Srivastava, Puneet; Pelosi, Anna; Chirico, Giovanni B.

2018-07-01

Reference evapotranspiration (ET0) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET0 forecasts. Impact of individual forecast variables on ET0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET0 forecast performances.

A statistical analysis of the effects of a uniform minimum drinking age

DOT National Transportation Integrated Search

1987-04-01

This report examines the relationship between minimum drinking age (MDA) and : highway fatalities during the 1975-1985 period, when 35 states changed their : MDAs. An econometric model of fatalities involving the 18-20 year-old driver : normalized by...

SPATIAL STATISTICS AND ECONOMETRICS FOR MODELS IN FISHERIES ECONOMICS. (R828012)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

78 FR 63267 - Self-Regulatory Organizations; The Options Clearing Corporation; Notice of No Objection to...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

... the set of risk factors whose behavior is included in the econometric models underlying STANS, time series of proportional changes in implied volatilities for a range of tenors and in-the-money and out-of...

Eliminating Problems Caused by Multicollinearity: A Warning.

ERIC Educational Resources Information Center

Kennedy, Peter E.

1982-01-01

Explains why an econometric practice introduced by J.C. Soper cannot eliminate the problems caused by multicollinearity. The author suggests that it can be a useful technique in that it forces researchers to pay more attention to the specifications of their models. (AM)

An Econometric Examination of the Behavioral Perspective Model in the Context of Norwegian Retailing

ERIC Educational Resources Information Center

Sigurdsson, Valdimar; Kahamseh, Saeed; Gunnarsson, Didrik; Larsen, Nils Magne; Foxall, Gordon R.

2013-01-01

The behavioral perspective model's (BPM; Foxall, 1990) retailing literature is built on extensive empirical research and techniques that were originally refined in choice experiments in behavioral economics and behavior analysis, and then tested mostly on British consumer panel data. We test the BPM in the context of Norwegian retailing. This…

Appendix : airborne incidents : an econometric analysis of severity

DOT National Transportation Integrated Search

2014-12-19

This is the Appendix for Airborne Incidents: An Econometric Analysis of Severity Report. : Airborne loss of separation incidents occur when an aircraft breaches the defined separation limit (vertical and/or horizontal) with another aircraft or terrai...

Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database, 1993-2006.

PubMed

Moran, John L; Solomon, Patricia J

2011-02-01

Time series analysis has seen limited application in the biomedical Literature. The utility of conventional and advanced time series estimators was explored for intensive care unit (ICU) outcome series. Monthly mean time series, 1993-2006, for hospital mortality, severity-of-illness score (APACHE III), ventilation fraction and patient type (medical and surgical), were generated from the Australia and New Zealand Intensive Care Society adult patient database. Analyses encompassed geographical seasonal mortality patterns, series structural time changes, mortality series volatility using autoregressive moving average and Generalized Autoregressive Conditional Heteroscedasticity models in which predicted variances are updated adaptively, and bivariate and multivariate (vector error correction models) cointegrating relationships between series. The mortality series exhibited marked seasonality, declining mortality trend and substantial autocorrelation beyond 24 lags. Mortality increased in winter months (July-August); the medical series featured annual cycling, whereas the surgical demonstrated long and short (3-4 months) cycling. Series structural breaks were apparent in January 1995 and December 2002. The covariance stationary first-differenced mortality series was consistent with a seasonal autoregressive moving average process; the observed conditional-variance volatility (1993-1995) and residual Autoregressive Conditional Heteroscedasticity effects entailed a Generalized Autoregressive Conditional Heteroscedasticity model, preferred by information criterion and mean model forecast performance. Bivariate cointegration, indicating long-term equilibrium relationships, was established between mortality and severity-of-illness scores at the database level and for categories of ICUs. Multivariate cointegration was demonstrated for {log APACHE III score, log ICU length of stay, ICU mortality and ventilation fraction}. A system approach to understanding series time-dependence may be established using conventional and advanced econometric time series estimators. © 2010 Blackwell Publishing Ltd.

Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test

PubMed Central

Jiang, Chuanjin; Zhang, Jing; Song, Fugen

2014-01-01

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability. PMID:24892061

Selecting single model in combination forecasting based on cointegration test and encompassing test.

PubMed

Jiang, Chuanjin; Zhang, Jing; Song, Fugen

2014-01-01

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability.

Water balance models in one-month-ahead streamflow forecasting

USGS Publications Warehouse

Alley, William M.

1985-01-01

Techniques are tested that incorporate information from water balance models in making 1-month-ahead streamflow forecasts in New Jersey. The results are compared to those based on simple autoregressive time series models. The relative performance of the models is dependent on the month of the year in question. The water balance models are most useful for forecasts of April and May flows. For the stations in northern New Jersey, the April and May forecasts were made in order of decreasing reliability using the water-balance-based approaches, using the historical monthly means, and using simple autoregressive models. The water balance models were useful to a lesser extent for forecasts during the fall months. For the rest of the year the improvements in forecasts over those obtained using the simpler autoregressive models were either very small or the simpler models provided better forecasts. When using the water balance models, monthly corrections for bias are found to improve minimum mean-square-error forecasts as well as to improve estimates of the forecast conditional distributions.

Spatial Econometric Research on the Relationship between Highway Construction and Regional Economic Growth in China: Evidence from the Nationwide Panel Data

NASA Astrophysics Data System (ADS)

Ye, N. J.; Li, W. J.; Li, Y.; Bai, Y. F.

2017-12-01

Based on spatial panel data from 2010 to 2016 in China, this paper makes an empirical analysis on the relationship between highway construction and regional economic growth by means of spatial econometric model. The results show that there is positive spatial correlation on regional economic growth in China, and strong spatial dependences between some provinces and cities appear, specifically, Hebei, Beijing, Tianjin, Shanghai, Zhejiang and other eastern coastal areas show high-high agglomeration trend, the Pearl River Delta region presents high-low agglomeration trend; In terms of nationwide provinces and municipalities, a province’s highway construction investment for their own province and the neighboring provinces has pulling effect on economic growth to a certain extent, and the direct effect is more obvious.

Evaluation Of Statistical Models For Forecast Errors From The HBV-Model

NASA Astrophysics Data System (ADS)

Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.

2009-04-01

Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.

Determining the Relationship Between Moral Waivers and Marine Corps Unsuitability Attrition

DTIC Science & Technology

2008-03-01

observed characteristics. However, econometric research indicates that the magnitude of interaction effects estimated via probit or logit models may...1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service separations. 15. NUMBER OF...files from fiscal years 1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service

Effects of land use change on soil carbon cycling in the conterminous United States from 1900 to 2050

Treesearch

Peter B. Woodbury; Linda S. Heath; James E. Smith

2007-01-01

We developed matrices representing historical area transitions between forest and other land uses. We projected future transitions on the basis of historical transitions and econometric model results. These matrices were used to drive a model of changes in soil and forest floor carbon stocks. Our model predicted net carbon emission from 1900 until 1982, then...

Model Study for an Economic Data Program on the Conditions of Arts and Cultural Institutions. Final Report.

ERIC Educational Resources Information Center

Deane, Robert T.; And Others

The development of econometric models and a data base to predict the responsiveness of arts institutions to changes in the economy is reported. The study focused on models for museums, theaters (profit and non-profit), symphony, ballet, opera, and dance. The report details four objectives of the project: to identify useful databases and studies on…

An integrated model of human-wildlife interdependence

USGS Publications Warehouse

John, Kun H.; Walsh, Richard G.; Johnson, R.L.

1994-01-01

This paper attempts to integrate wildlife-related ecologic and economic variables into an econometric model. The model reveals empirical evidence of the presumed interdependence of human-wildlife and the holistic nature of humanity's relationship to the ecosystem. Human use of biologic resources varies not only with income, education, and population, but also with sustainability of humankind's action relative to the quality and quantity of the supporting ecological base.

Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model

PubMed Central

Zhang, Jinlun

2015-01-01

Abstract Arctic sea ice drift forecasts of 6 h–9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h–8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high‐resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast. PMID:27818852

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast

LEAMram (Trademark): Land Use Evolution and Impact Assessment Model Residential Attractiveness Model

DTIC Science & Technology

2006-09-01

MEPLAN are popular in both the United States and overseas, and focus on identifying growth by income and housing costs. These and other models focus...CUF-2), SLEUTH, Landuse Evolution Assessment Model (LEAM™), Smart Places, and What If?: • CUF-2 uses a set of econometric models to project...ER D C/ CE R L TR -0 6 -2 8 LEAMram™: Land use Evolution and impact Assessment Model Residential Attractiveness Model James D

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoff, Thomas Hoff; Kankiewicz, Adam

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP)more » forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”« less

Econometric Model of Rice Policy Based On Presidential Instruction

NASA Astrophysics Data System (ADS)

Abadi Sembiring, Surya; Hutauruk, Julia

2018-01-01

The objective of research is to build an econometric model based on Presidential Instruction rice policy. The data was monthly time series from March 2005 to September 2009. Rice policy model specification using simultaneous equation, consisting of 14 structural equations and four identity equation, which was estimated using Two Stages Least Squares (2SLS) method. The results show that: (1) an increase of government purchasing price of dried harvest paddy has a positive impact on to increase in total rice production and community rice stock, (2) an increase community rice stock lead to decrease the rice imports, (3) an increase of the realization of the distribution of subsidized ZA fertilizers and the realization of the distribution of subsidized NPK fertilizers has a positive impact on to increase in total rice production and community rice stock and to reduce rice imports, (4) the price of the dried harvest paddy is highly responsive to the water content of dried harvest paddy both the short run and long run, (5) the quantity of rice imported is highly responsive to the imported rice price, both short run and long run.

Groundwater economics: An object-oriented foundation for integrated studies of irrigated agricultural systems

USDA-ARS?s Scientific Manuscript database

An integrated foundation is presented to study the impacts of external forcings on irrigated agricultural systems. Individually, models are presented that simulate groundwater hydrogeology and econometric farm level crop choices and irrigated water use. The natural association between groundwater we...

Adaptive Elastic Net for Generalized Methods of Moments.

PubMed

Caner, Mehmet; Zhang, Hao Helen

2014-01-30

Model selection and estimation are crucial parts of econometrics. This paper introduces a new technique that can simultaneously estimate and select the model in generalized method of moments (GMM) context. The GMM is particularly powerful for analyzing complex data sets such as longitudinal and panel data, and it has wide applications in econometrics. This paper extends the least squares based adaptive elastic net estimator of Zou and Zhang (2009) to nonlinear equation systems with endogenous variables. The extension is not trivial and involves a new proof technique due to estimators lack of closed form solutions. Compared to Bridge-GMM of Caner (2009), we allow for the number of parameters to diverge to infinity as well as collinearity among a large number of variables, also the redundant parameters set to zero via a data dependent technique. This method has the oracle property, meaning that we can estimate nonzero parameters with their standard limit and the redundant parameters are dropped from the equations simultaneously. Numerical examples are used to illustrate the performance of the new method.

Travel cost demand model based river recreation benefit estimates with on-site and household surveys: Comparative results and a correction procedure

NASA Astrophysics Data System (ADS)

Loomis, John

2003-04-01

Past recreation studies have noted that on-site or visitor intercept surveys are subject to over-sampling of avid users (i.e., endogenous stratification) and have offered econometric solutions to correct for this. However, past papers do not estimate the empirical magnitude of the bias in benefit estimates with a real data set, nor do they compare the corrected estimates to benefit estimates derived from a population sample. This paper empirically examines the magnitude of the recreation benefits per trip bias by comparing estimates from an on-site river visitor intercept survey to a household survey. The difference in average benefits is quite large, with the on-site visitor survey yielding 24 per day trip, while the household survey yields 9.67 per day trip. A simple econometric correction for endogenous stratification in our count data model lowers the benefit estimate to $9.60 per day trip, a mean value nearly identical and not statistically different from the household survey estimate.

Modeling the assessment of the economic factors impact on the development of social entrepreneurship

NASA Astrophysics Data System (ADS)

Absalyamov, T.; Kundakchyan, R.; Zulfakarova, L.; Zapparova, Z.

2017-12-01

The article deals with the research of modern trends in the development of social entrepreneurship in Russia. The results of the research allow the authors to identify a system of factors that affect the development of entrepreneurship in the modern Russian economy. Moreover, the authors argue the regional specificity of the development of social entrepreneurship. The paper considers specific features and formulates the main limitations of the development of entrepreneurship and the competitive environment in the social sphere. The authors suggest an econometric model for assessing the influence of economic factors on the development of socially-oriented entrepreneurship and present an algorithm for calculating its components. The results of the econometric analysis identify the main factors of the change in the performance indicators of entrepreneurial activity and determine the degree of their impact on social entrepreneurship. The results and conclusions can serve as an estimation of the socioeconomic consequences of the sustainability disruption of the entrepreneurial potential realization in the social sphere.

Incomes, Attitudes, and Occurrences of Invasive Species: An Application to Signal Crayfish in Sweden

NASA Astrophysics Data System (ADS)

Gren, Ing-Marie; Campos, Monica; Edsman, Lennart; Bohman, Patrik

2009-02-01

This article analyzes and carries out an econometric test of the explanatory power of economic and attitude variables for occurrences of the nonnative signal crayfish in Swedish waters. Signal crayfish are a carrier of plague which threatens the native noble crayfish with extinction. Crayfish are associated with recreational and cultural traditions in Sweden, which may run against environmental preferences for preserving native species. Econometric analysis is carried out using panel data at the municipality level with economic factors and attitudes as explanatory variables, which are derived from a simple dynamic harvesting model. A log-normal model is used for the regression analysis, and the results indicate significant impacts on occurrences of waters with signal crayfish of changes in both economic and attitude variables. Variables reflecting environmental and recreational preferences have unexpected signs, where the former variable has a positive and the latter a negative impact on occurrences of waters with signal crayfish. These effects are, however, counteracted by their respective interaction effect with income.

Forecasting biodiversity in breeding birds using best practices

PubMed Central

Taylor, Shawn D.; White, Ethan P.

2018-01-01

Biodiversity forecasts are important for conservation, management, and evaluating how well current models characterize natural systems. While the number of forecasts for biodiversity is increasing, there is little information available on how well these forecasts work. Most biodiversity forecasts are not evaluated to determine how well they predict future diversity, fail to account for uncertainty, and do not use time-series data that captures the actual dynamics being studied. We addressed these limitations by using best practices to explore our ability to forecast the species richness of breeding birds in North America. We used hindcasting to evaluate six different modeling approaches for predicting richness. Hindcasts for each method were evaluated annually for a decade at 1,237 sites distributed throughout the continental United States. All models explained more than 50% of the variance in richness, but none of them consistently outperformed a baseline model that predicted constant richness at each site. The best practices implemented in this study directly influenced the forecasts and evaluations. Stacked species distribution models and “naive” forecasts produced poor estimates of uncertainty and accounting for this resulted in these models dropping in the relative performance compared to other models. Accounting for observer effects improved model performance overall, but also changed the rank ordering of models because it did not improve the accuracy of the “naive” model. Considering the forecast horizon revealed that the prediction accuracy decreased across all models as the time horizon of the forecast increased. To facilitate the rapid improvement of biodiversity forecasts, we emphasize the value of specific best practices in making forecasts and evaluating forecasting methods. PMID:29441230

Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS)

NASA Astrophysics Data System (ADS)

OConnor, A.; Kirtman, B. P.; Harrison, S.; Gorman, J.

2016-02-01

Current US Navy forecasting systems cannot easily incorporate extended-range forecasts that can improve mission readiness and effectiveness; ensure safety; and reduce cost, labor, and resource requirements. If Navy operational planners had systems that incorporated these forecasts, they could plan missions using more reliable and longer-term weather and climate predictions. Further, using multi-model forecast ensembles instead of single forecasts would produce higher predictive performance. Extended-range multi-model forecast ensembles, such as those available in the North American Multi-Model Ensemble (NMME), are ideal for system integration because of their high skill predictions; however, even higher skill predictions can be produced if forecast model ensembles are combined correctly. While many methods for weighting models exist, the best method in a given environment requires expert knowledge of the models and combination methods.We present an innovative approach that uses machine learning to combine extended-range predictions from multi-model forecast ensembles and generate a probabilistic forecast for any region of the globe up to 12 months in advance. Our machine-learning approach uses 30 years of hindcast predictions to learn patterns of forecast model successes and failures. Each model is assigned a weight for each environmental condition, 100 km2 region, and day given any expected environmental information. These weights are then applied to the respective predictions for the region and time of interest to effectively stitch together a single, coherent probabilistic forecast. Our experimental results demonstrate the benefits of our approach to produce extended-range probabilistic forecasts for regions and time periods of interest that are superior, in terms of skill, to individual NMME forecast models and commonly weighted models. The probabilistic forecast leverages the strengths of three NMME forecast models to predict environmental conditions for an area spanning from San Diego, CA to Honolulu, HI, seven months in-advance. Key findings include: weighted combinations of models are strictly better than individual models; machine-learned combinations are especially better; and forecasts produced using our approach have the highest rank probability skill score most often.

Study protocol: combining experimental methods, econometrics and simulation modelling to determine price elasticities for studying food taxes and subsidies (The Price ExaM Study).

PubMed

Waterlander, Wilma E; Blakely, Tony; Nghiem, Nhung; Cleghorn, Christine L; Eyles, Helen; Genc, Murat; Wilson, Nick; Jiang, Yannan; Swinburn, Boyd; Jacobi, Liana; Michie, Jo; Ni Mhurchu, Cliona

2016-07-19

There is a need for accurate and precise food price elasticities (PE, change in consumer demand in response to change in price) to better inform policy on health-related food taxes and subsidies. The Price Experiment and Modelling (Price ExaM) study aims to: I) derive accurate and precise food PE values; II) quantify the impact of price changes on quantity and quality of discrete food group purchases and; III) model the potential health and disease impacts of a range of food taxes and subsidies. To achieve this, we will use a novel method that includes a randomised Virtual Supermarket experiment and econometric methods. Findings will be applied in simulation models to estimate population health impact (quality-adjusted life-years [QALYs]) using a multi-state life-table model. The study will consist of four sequential steps: 1. We generate 5000 price sets with random price variation for all 1412 Virtual Supermarket food and beverage products. Then we add systematic price variation for foods to simulate five taxes and subsidies: a fruit and vegetable subsidy and taxes on sugar, saturated fat, salt, and sugar-sweetened beverages. 2. Using an experimental design, 1000 adult New Zealand shoppers complete five household grocery shops in the Virtual Supermarket where they are randomly assigned to one of the 5000 price sets each time. 3. Output data (i.e., multiple observations of price configurations and purchased amounts) are used as inputs to econometric models (using Bayesian methods) to estimate accurate PE values. 4. A disease simulation model will be run with the new PE values as inputs to estimate QALYs gained and health costs saved for the five policy interventions. The Price ExaM study has the potential to enhance public health and economic disciplines by introducing internationally novel scientific methods to estimate accurate and precise food PE values. These values will be used to model the potential health and disease impacts of various food pricing policy options. Findings will inform policy on health-related food taxes and subsidies. Australian New Zealand Clinical Trials Registry ACTRN12616000122459 (registered 3 February 2016).

Airborne incidents : an econometric analysis of severity, December 31, 2014 : technical summary

DOT National Transportation Integrated Search

2014-12-31

This is a technical summary of the Airborne Incidents: An Econometric Analysis of Severity main report. : Airborne loss of separation incidents occur when an aircraft breaches the defined separation limit (vertical and/or horizontal) with anoth...

Software forecasting as it is really done: A study of JPL software engineers

NASA Technical Reports Server (NTRS)

Griesel, Martha Ann; Hihn, Jairus M.; Bruno, Kristin J.; Fouser, Thomas J.; Tausworthe, Robert C.

1993-01-01

This paper presents a summary of the results to date of a Jet Propulsion Laboratory internally funded research task to study the costing process and parameters used by internally recognized software cost estimating experts. Protocol Analysis and Markov process modeling were used to capture software engineer's forecasting mental models. While there is significant variation between the mental models that were studied, it was nevertheless possible to identify a core set of cost forecasting activities, and it was also found that the mental models cluster around three forecasting techniques. Further partitioning of the mental models revealed clustering of activities, that is very suggestive of a forecasting lifecycle. The different forecasting methods identified were based on the use of multiple-decomposition steps or multiple forecasting steps. The multiple forecasting steps involved either forecasting software size or an additional effort forecast. Virtually no subject used risk reduction steps in combination. The results of the analysis include: the identification of a core set of well defined costing activities, a proposed software forecasting life cycle, and the identification of several basic software forecasting mental models. The paper concludes with a discussion of the implications of the results for current individual and institutional practices.

Toward a Rational Educational Policy. An Econometric Analysis of Ontario, Canada, 1950-65 with Tests 1966-68 and Projections 1969-75.

ERIC Educational Resources Information Center

Handa, M. L.

This report describes some models the author developed to investigate the simultaneous interaction of decisionmakers in a province-wide educational system and to help formulate educational policy for achieving specified enrollments and expenditures. In chapter one, the author describes the models that examine the process of simultaneous…

Taxes in a Labor Supply Model with Joint Wage-Hours Determination.

ERIC Educational Resources Information Center

Rosen, Harvey S.

1976-01-01

Payroll and progressive income taxes play an enormous role in the American fiscal system. The purpose of this study is to present some econometric evidence on the effects of taxes on married women, a group of growing importance in the American labor force. A testable model of labor supply is developed which permits statistical estimation of a…

Counteracting structural errors in ensemble forecast of influenza outbreaks.

PubMed

Pei, Sen; Shaman, Jeffrey

2017-10-13

For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.

An Assessment of Japanese Carbon Tax Reform Using the E3MG Econometric Model

PubMed Central

Lee, Soocheol; Pollitt, Hector; Ueta, Kazuhiro

2012-01-01

This paper analyses the potential economic and environmental effects of carbon taxation in Japan using the E3MG model, a global macroeconometric model constructed by the University of Cambridge and Cambridge Econometrics. The paper approaches the issues by considering first the impacts of the carbon tax in Japan introduced in 2012 and then the measures necessary to reduce Japan's emissions in line with its Copenhagen pledge of −25% compared to 1990 levels. The results from the model suggest that FY2012 Tax Reform has only a small impact on emission levels and no significant impact on GDP and employment. The potential costs of reducing emissions to meet the 25% reduction target for 2020 are quite modest, but noticeable. GDP falls by around 1.2% compared to the baseline and employment by 0.4% compared to the baseline. But this could be offset, with some potential economic benefits, if revenues are recycled efficiently. This paper considers two revenue recycling scenarios. The most positive outcome is if revenues are used both to reduce income tax rates and to increase investment in energy efficiency. This paper shows there could be double dividend effects, if Carbon Tax Reform is properly designed. PMID:23365531

Attrition Bias in Panel Data: A Sheep in Wolf's Clothing? A Case Study Based on the Mabel Survey.

PubMed

Cheng, Terence C; Trivedi, Pravin K

2015-09-01

This paper investigates the nature and consequences of sample attrition in a unique longitudinal survey of medical doctors. We describe the patterns of non-response and examine if attrition affects the econometric analysis of medical labour market outcomes using the estimation of physician earnings equations as a case study. We compare the econometric gestimates obtained from a number of different modelling strategies, which are as follows: balanced versus unbalanced samples; an attrition model for panel data based on the classic sample selection model; and a recently developed copula-based selection model. Descriptive evidence shows that doctors who work longer hours, have lower years of experience, are overseas trained and have changed their work location are more likely to drop out. Our analysis suggests that the impact of attrition on inference about the earnings of general practitioners is small. For specialists, there appears to be some evidence for an economically significant bias. Finally, we discuss how the top-up samples in the Medicine in Australia: Balancing Employment and Life survey can be used to address the problem of panel attrition. Copyright © 2015 John Wiley & Sons, Ltd.

Groundwater economics: An object-oriented foundation for integrated studies of irrigated agricultural systems

NASA Astrophysics Data System (ADS)

Steward, David R.; Peterson, Jeffrey M.; Yang, Xiaoying; Bulatewicz, Tom; Herrera-Rodriguez, Mauricio; Mao, Dazhi; Hendricks, Nathan

2009-05-01

An integrated foundation is presented to study the impacts of external forcings on irrigated agricultural systems. Individually, models are presented that simulate groundwater hydrogeology and econometric farm level crop choices and irrigated water use. The natural association between groundwater wells and agricultural parcels is employed to couple these models using geographic information science technology and open modeling interface protocols. This approach is used to study the collective action problem of the common pool. Three different policies (existing, regulation, and incentive based) are studied in the semiarid grasslands overlying the Ogallala Aquifer in the central United States. Results show that while regulation using the prior appropriation doctrine and incentives using a water buy-back program may each achieve the same level of water savings across the study region, each policy has a different impact on spatial patterns of groundwater declines and farm level economic activity. This represents the first time that groundwater and econometric models of irrigated agriculture have been integrated at the well-parcel level and provides methods for scientific investigation of this coupled natural-human system. Results are useful for science to inform decision making and public policy debate.

An assessment of Japanese carbon tax reform using the E3MG econometric model.

PubMed

Lee, Soocheol; Pollitt, Hector; Ueta, Kazuhiro

2012-01-01

This paper analyses the potential economic and environmental effects of carbon taxation in Japan using the E3MG model, a global macroeconometric model constructed by the University of Cambridge and Cambridge Econometrics. The paper approaches the issues by considering first the impacts of the carbon tax in Japan introduced in 2012 and then the measures necessary to reduce Japan's emissions in line with its Copenhagen pledge of -25% compared to 1990 levels. The results from the model suggest that FY2012 Tax Reform has only a small impact on emission levels and no significant impact on GDP and employment. The potential costs of reducing emissions to meet the 25% reduction target for 2020 are quite modest, but noticeable. GDP falls by around 1.2% compared to the baseline and employment by 0.4% compared to the baseline. But this could be offset, with some potential economic benefits, if revenues are recycled efficiently. This paper considers two revenue recycling scenarios. The most positive outcome is if revenues are used both to reduce income tax rates and to increase investment in energy efficiency. This paper shows there could be double dividend effects, if Carbon Tax Reform is properly designed.

The regional economic impacts of bypasses : a longitudinal study incorporating spatial panel econometrics and multilevel modeling.

DOT National Transportation Integrated Search

2011-09-01

This paper will describe an integrated approach to documenting and quantifying the impacts of bypasses : on small communities, with a focus on what economic impacts, if any, occur, and how these impacts : change over time. Two similarly sized communi...

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Two approaches to forecast Ebola synthetic epidemics.

PubMed

Champredon, David; Li, Michael; Bolker, Benjamin M; Dushoff, Jonathan

2018-03-01

We use two modelling approaches to forecast synthetic Ebola epidemics in the context of the RAPIDD Ebola Forecasting Challenge. The first approach is a standard stochastic compartmental model that aims to forecast incidence, hospitalization and deaths among both the general population and health care workers. The second is a model based on the renewal equation with latent variables that forecasts incidence in the whole population only. We describe fitting and forecasting procedures for each model and discuss their advantages and drawbacks. We did not find that one model was consistently better in forecasting than the other. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

NASA Astrophysics Data System (ADS)

Rogelis, María Carolina; Werner, Micha

2018-02-01

Numerical weather prediction (NWP) models are fundamental to extend forecast lead times beyond the concentration time of a watershed. Particularly for flash flood forecasting in tropical mountainous watersheds, forecast precipitation is required to provide timely warnings. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that bias correction can provide, in a tropical mountainous area. The paper focuses on the comparison of streamflows obtained from the post-processed precipitation forecasts, particularly the comparison of ensemble forecasts and their potential in providing skilful flood forecasts. The Weather Research and Forecasting (WRF) model is used to produce precipitation forecasts that are post-processed and used to drive a hydrologic model. Discharge forecasts obtained from the hydrological model are used to assess the skill of the WRF model. The results show that post-processed WRF precipitation adds value to the flood early warning system when compared to zero-precipitation forecasts, although the precipitation forecast used in this analysis showed little added value when compared to climatology. However, the reduction of biases obtained from the post-processed ensembles show the potential of this method and model to provide usable precipitation forecasts in tropical mountainous watersheds. The need for more detailed evaluation of the WRF model in the study area is highlighted, particularly the identification of the most suitable parameterisation, due to the inability of the model to adequately represent the convective precipitation found in the study area.

A Local Forecast of Land Surface Wetness Conditions, Drought, and St. Louis Encephalitis Virus Transmission Derived from Seasonal Climate Predictions

NASA Astrophysics Data System (ADS)

Shaman, J.; Stieglitz, M.; Zebiak, S.; Cane, M.; Day, J. F.

2002-12-01

We present an ensemble local hydrologic forecast derived from the seasonal forecasts of the International Research Institute (IRI) for Climate Prediction. Three- month seasonal forecasts were used to resample historical meteorological conditions and generate ensemble forcing datasets for a TOPMODEL-based hydrology model. Eleven retrospective forecasts were run at a Florida and New York site. Forecast skill was assessed for mean area modeled water table depth (WTD), i.e. near surface soil wetness conditions, and compared with WTD simulated with observed data. Hydrology model forecast skill was evident at the Florida site but not at the New York site. At the Florida site, persistence of hydrologic conditions and local skill of the IRI seasonal forecast contributed to the local hydrologic forecast skill. This forecast will permit probabilistic prediction of future hydrologic conditions. At the Florida site, we have also quantified the link between modeled WTD (i.e. drought) and the amplification and transmission of St. Louis Encephalitis virus (SLEV). We derive an empirical relationship between modeled land surface wetness and levels of SLEV transmission associated with human clinical cases. We then combine the seasonal forecasts of local, modeled WTD with this empirical relationship and produce retrospective probabilistic seasonal forecasts of epidemic SLEV transmission in Florida. Epidemic SLEV transmission forecast skill is demonstrated. These findings will permit real-time forecast of drought and resultant SLEV transmission in Florida.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis

NASA Astrophysics Data System (ADS)

Mohamed Ismael, Hawa; Vandyck, George Kobina

The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.

Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

NASA Astrophysics Data System (ADS)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the MizuRoute channel routing tool) but also distributed model states such as soil moisture and snow water equivalent. We also describe challenges in distributed model-based forecasting, including the application and early results of real-time hydrologic data assimilation.

Forecasting Dust Storms Using the CARMA-Dust Model and MM5 Weather Data

NASA Astrophysics Data System (ADS)

Barnum, B. H.; Winstead, N. S.; Wesely, J.; Hakola, A.; Colarco, P.; Toon, O. B.; Ginoux, P.; Brooks, G.; Hasselbarth, L. M.; Toth, B.; Sterner, R.

2002-12-01

An operational model for the forecast of dust storms in Northern Africa, the Middle East and Southwest Asia has been developed for the United States Air Force Weather Agency (AFWA). The dust forecast model uses the 5th generation Penn State Mesoscale Meteorology Model (MM5), and a modified version of the Colorado Aerosol and Radiation Model for Atmospheres (CARMA). AFWA conducted a 60 day evaluation of the dust model to look at the model's ability to forecast dust storms for short, medium and long range (72 hour) forecast periods. The study used satellite and ground observations of dust storms to verify the model's effectiveness. Each of the main mesoscale forecast theaters was broken down into smaller sub-regions for detailed analysis. The study found the forecast model was able to forecast dust storms in Saharan Africa and the Sahel region with an average Probability of Detection (POD)exceeding 68%, with a 16% False Alarm Rate (FAR). The Southwest Asian theater had average POD's of 61% with FAR's averaging 10%.

Research on light rail electric load forecasting based on ARMA model

NASA Astrophysics Data System (ADS)

Huang, Yifan

2018-04-01

The article compares a variety of time series models and combines the characteristics of power load forecasting. Then, a light load forecasting model based on ARMA model is established. Based on this model, a light rail system is forecasted. The prediction results show that the accuracy of the model prediction is high.

The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

NASA Astrophysics Data System (ADS)

Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

2013-10-01

Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

Systems GMM estimates of the health care spending and GDP relationship: a note.

PubMed

Kumar, Saten

2013-06-01

This paper utilizes the systems generalized method of moments (GMM) [Arellano and Bover (1995) J Econometrics 68:29-51; Blundell and Bond (1998) J Econometrics 87:115-143], and panel Granger causality [Hurlin and Venet (2001) Granger Causality tests in panel data models with fixed coefficients. Mime'o, University Paris IX], to investigate the health care spending and gross domestic product (GDP) relationship for organisation for economic co-operation and development countries over the period 1960-2007. The system GMM estimates confirm that the contribution of real GDP to health spending is significant and positive. The panel Granger causality tests imply that a bi-directional causality exists between health spending and GDP. To this end, policies aimed at raising health spending will eventually improve the well-being of the population in the long run.

Computationally intensive econometrics using a distributed matrix-programming language.

PubMed

Doornik, Jurgen A; Hendry, David F; Shephard, Neil

2002-06-15

This paper reviews the need for powerful computing facilities in econometrics, focusing on concrete problems which arise in financial economics and in macroeconomics. We argue that the profession is being held back by the lack of easy-to-use generic software which is able to exploit the availability of cheap clusters of distributed computers. Our response is to extend, in a number of directions, the well-known matrix-programming interpreted language Ox developed by the first author. We note three possible levels of extensions: (i) Ox with parallelization explicit in the Ox code; (ii) Ox with a parallelized run-time library; and (iii) Ox with a parallelized interpreter. This paper studies and implements the first case, emphasizing the need for deterministic computing in science. We give examples in the context of financial economics and time-series modelling.

Forecasting in foodservice: model development, testing, and evaluation.

PubMed

Miller, J L; Thompson, P A; Orabella, M M

1991-05-01

This study was designed to develop, test, and evaluate mathematical models appropriate for forecasting menu-item production demand in foodservice. Data were collected from residence and dining hall foodservices at Ohio State University. Objectives of the study were to collect, code, and analyze the data; develop and test models using actual operation data; and compare forecasting results with current methods in use. Customer count was forecast using deseasonalized simple exponential smoothing. Menu-item demand was forecast by multiplying the count forecast by a predicted preference statistic. Forecasting models were evaluated using mean squared error, mean absolute deviation, and mean absolute percentage error techniques. All models were more accurate than current methods. A broad spectrum of forecasting techniques could be used by foodservice managers with access to a personal computer and spread-sheet and database-management software. The findings indicate that mathematical forecasting techniques may be effective in foodservice operations to control costs, increase productivity, and maximize profits.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

NASA Astrophysics Data System (ADS)

Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

2015-12-01

Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

Combining forecast weights: Why and how?

NASA Astrophysics Data System (ADS)

Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

2012-09-01

This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

77 FR 71788 - Notice of Change to the Publication of Natural Gas Wellhead Prices

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-04

... gas wellhead price using a time-series econometric model, which incorporates data from historical... (DOE). ACTION: Notice of a discontinuation of series in the publication of natural gas wellhead prices... price series. Beginning in January 2013, EIA will discontinue publishing wellhead prices, and will begin...

Spatial analysis of rural land development

Treesearch

Seong-Hoon Cho; David H. Newman

2005-01-01

This article examines patterns of rural land development and density using spatial econometric models with the application of Geographical Information System (GIS). The cluster patterns of both development and high-density development indicate that the spatially continuous expansions of development and high-density development exist in relatively remote rural areas....

The Insulation Board Industry - An Economic Analysis

Treesearch

Albert T. Schuler

1978-01-01

An econometric model of the domestic insulation board industry was developed to identify and quantify the major factors affecting quantity consumed and price. The factors identified were housing starts, residential improvement activity, disposable personal income, productivity, pulpwood and residue prices, and power costs. Disposable personal income was the most...

An Econommetric Analysis of the U.S. Hardboard Market

Treesearch

Albert T. Schuler

1978-01-01

An econometric model of U.S. hardboard consumption was developed to identify the major variables affecting hardboard consumption and price. The variables identified were housing starts, residential improvement activity, disposable personal income, hardwood plywood price, productivity, pulpwood and residue price, hardboard tariff, and power cost, Disposable personal...

The Determinants of Girls' Educational Enrollment in Ghana. Working Paper.

ERIC Educational Resources Information Center

Johnson, Rebecca; Kyle, Steven

This study examined the determinants of school enrollment in Ghana, considering historical and social information to formulate an econometric model of school enrollment patterns for households. Data came from a 1989 survey of households in Ghana. The survey collected basic information about community characteristics, health and school facilities,…

Groundwater response to changing water-use practices in sloping aquifers using convolution of transient response functions

USDA-ARS?s Scientific Manuscript database

An integrated foundation is presented to study the impacts of external forcings on irrigated agricultural systems. Individually, models are presented that simulate groundwater hydrogeology and econometric farm level crop choices and irrigated water use. The natural association between groundwater we...

Statistical Cost Estimation in Higher Education: Some Alternatives.

ERIC Educational Resources Information Center

Brinkman, Paul T.; Niwa, Shelley

Recent developments in econometrics that are relevant to the task of estimating costs in higher education are reviewed. The relative effectiveness of alternative statistical procedures for estimating costs are also tested. Statistical cost estimation involves three basic parts: a model, a data set, and an estimation procedure. Actual data are used…

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

PubMed

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.

A Wind Forecasting System for Energy Application

NASA Astrophysics Data System (ADS)

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2010-05-01

Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated probabilistic wind forecasts which will be invaluable in wind energy management. In brief, this method turns the ensemble forecasts into a calibrated predictive probability distribution. Each ensemble member is provided with a 'weight' determined by its relative predictive skill over a training period of around 30 days. Verification of data is carried out using observed wind data from operational wind farms. These are then compared to existing forecasts produced by ECMWF and Met Eireann in relation to skill scores. We are developing decision-making models to show the benefits achieved using the data produced by our wind energy forecasting system. An energy trading model will be developed, based on the rules currently used by the Single Electricity Market Operator for energy trading in Ireland. This trading model will illustrate the potential for financial savings by using the forecast data generated by this research.

Time series regression and ARIMAX for forecasting currency flow at Bank Indonesia in Sulawesi region

NASA Astrophysics Data System (ADS)

Suharsono, Agus; Suhartono, Masyitha, Aulia; Anuravega, Arum

2015-12-01

The purpose of the study is to forecast the outflow and inflow of currency at Indonesian Central Bank or Bank Indonesia (BI) in Sulawesi Region. The currency outflow and inflow data tend to have a trend pattern which is influenced by calendar variation effects. Therefore, this research focuses to apply some forecasting methods that could handle calendar variation effects, i.e. Time Series Regression (TSR) and ARIMAX models, and compare the forecast accuracy with ARIMA model. The best model is selected based on the lowest of Root Mean Squares Errors (RMSE) at out-sample dataset. The results show that ARIMA is the best model for forecasting the currency outflow and inflow at South Sulawesi. Whereas, the best model for forecasting the currency outflow at Central Sulawesi and Southeast Sulawesi, and for forecasting the currency inflow at South Sulawesi and North Sulawesi is TSR. Additionally, ARIMAX is the best model for forecasting the currency outflow at North Sulawesi. Hence, the results show that more complex models do not neccessary yield more accurate forecast than the simpler one.

Gambling scores for earthquake predictions and forecasts

NASA Astrophysics Data System (ADS)

Zhuang, Jiancang

2010-04-01

This paper presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points betted by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. We also calculate the upper bound of the gambling score when the true model is a renewal process, the stress release model or the ETAS model and when the reference model is the Poisson model.

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

Robust Bounded Influence Tests in Linear Models

DTIC Science & Technology

1988-11-01

sensitivity analysis and bounded influence estimation. In: Evaluation of Econometric Models, J. Kmenta and J.B. Ramsey (eds.) Academic Press, New York...1R’OBUST bOUNDED INFLUENCE TESTS IN LINEA’ MODELS and( I’homas P. [lettmansperger* Tim [PennsylvanLa State UJniversity A M i0d fix pu111 rsos.p JJ 1 0...November 1988 ROBUST BOUNDED INFLUENCE TESTS IN LINEAR MODELS Marianthi Markatou The University of Iowa and Thomas P. Hettmansperger* The Pennsylvania

Sensitivity of a Simulated Derecho Event to Model Initial Conditions

NASA Astrophysics Data System (ADS)

Wang, Wei

2014-05-01

Since 2003, the MMM division at NCAR has been experimenting cloud-permitting scale weather forecasting using Weather Research and Forecasting (WRF) model. Over the years, we've tested different model physics, and tried different initial and boundary conditions. Not surprisingly, we found that the model's forecasts are more sensitive to the initial conditions than model physics. In 2012 real-time experiment, WRF-DART (Data Assimilation Research Testbed) at 15 km was employed to produce initial conditions for twice-a-day forecast at 3 km. On June 29, this forecast system captured one of the most destructive derecho event on record. In this presentation, we will examine forecast sensitivity to different model initial conditions, and try to understand the important features that may contribute to the success of the forecast.

Density Deconvolution With EPI Splines

DTIC Science & Technology

2015-09-01

effects of various substances on test subjects [11], [12]. Whereas in geophysics, a shot may be fired into the ground, in pharmacokinetics, a signal is...be significant, including medicine, bioinformatics, chemistry, as- tronomy, and econometrics , as well as an extensive review of kernel based methods...demonstrate the effectiveness of our model in simulations motivated by test instances in [32]. We consider an additive measurement model scenario where

An Econometric Model of External Labor Supply to the Establishment Within a Confined Geographic Market.

ERIC Educational Resources Information Center

Hines, Robert James

The study conducted in the Buffalo, New York standard metropolitan statistical area, was undertaken to formulate and test a simple model of labor supply for a local labor market. The principal variables to be examined to determine the external supply function of labor to the establishment are variants of the rate of change of the entry wage and…

An Econometric Model for Estimating IQ Scores and Environmental Influences on the Pattern of IQ Scores Over Time.

ERIC Educational Resources Information Center

Kadane, Joseph B.; And Others

This paper offers a preliminary analysis of the effects of a semi-segregated school system on the IQ's of its students. The basic data consist of IQ scores for fourth, sixth, and eighth grades and associated environmental data obtained from their school records. A statistical model is developed to analyze longitudinal data when both process error…

An Investigation of Multivariate Adaptive Regression Splines for Modeling and Analysis of Univariate and Semi-Multivariate Time Series Systems

DTIC Science & Technology

1991-09-01

However, there is no guarantee that this would work; for instance if the data were generated by an ARCH model (Tong, 1990 pp. 116-117) then a simple...Hill, R., Griffiths, W., Lutkepohl, H., and Lee, T., Introduction to the Theory and Practice of Econometrics , 2th ed., Wiley, 1985. Kendall, M., Stuart

Dynamics of Markets

NASA Astrophysics Data System (ADS)

McCauley, Joseph L.

2009-09-01

Preface; 1. Econophysics: why and what; 2. Neo-classical economic theory; 3. Probability and stochastic processes; 4. Introduction to financial economics; 5. Introduction to portfolio selection theory; 6. Scaling, pair correlations, and conditional densities; 7. Statistical ensembles: deducing dynamics from time series; 8. Martingale option pricing; 9. FX market globalization: evolution of the dollar to worldwide reserve currency; 10. Macroeconomics and econometrics: regression models vs. empirically based modeling; 11. Complexity; Index.

Provisioning in Agricultural Communities: Local, Regional and Global Cereal Prices and Local Production on Three Continents

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Tondel, Fabien; Essam, Timothy; Thorne, Jennifer A.; Mann, Bristol F.; Eilerts, Gary

2012-01-01

Monitoring and incorporating diverse market and staple food information into food price indices is critical for food price analyses. Satellite remote sensing data and earth science models have an important role to play in improving humanitarian aid timing, delivery and distribution. Incorporating environmental observations into econometric models will improve food security analysis and understanding of market functioning.

Potential Impact of Increased Numbers of Physicians upon Physician Behavior, Access to, and Cost of, Medical Care. Final Report.

ERIC Educational Resources Information Center

Musgrave, Gerald L.

The potential impact of the increasing supply of physicians on physician behavior, the cost of medical services, and access to services is addressed in detail in this final research report. Econometric modeling and analyses of economic activity within the health sector were undertaken. An eight equation model of the hospital and physician sectors…

Ability of matrix models to explain the past and predict the future of plant populations.

USGS Publications Warehouse

McEachern, Kathryn; Crone, Elizabeth E.; Ellis, Martha M.; Morris, William F.; Stanley, Amanda; Bell, Timothy; Bierzychudek, Paulette; Ehrlen, Johan; Kaye, Thomas N.; Knight, Tiffany M.; Lesica, Peter; Oostermeijer, Gerard; Quintana-Ascencio, Pedro F.; Ticktin, Tamara; Valverde, Teresa; Williams, Jennifer I.; Doak, Daniel F.; Ganesan, Rengaian; Thorpe, Andrea S.; Menges, Eric S.

2013-01-01

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.

Ability of matrix models to explain the past and predict the future of plant populations.

PubMed

Crone, Elizabeth E; Ellis, Martha M; Morris, William F; Stanley, Amanda; Bell, Timothy; Bierzychudek, Paulette; Ehrlén, Johan; Kaye, Thomas N; Knight, Tiffany M; Lesica, Peter; Oostermeijer, Gerard; Quintana-Ascencio, Pedro F; Ticktin, Tamara; Valverde, Teresa; Williams, Jennifer L; Doak, Daniel F; Ganesan, Rengaian; McEachern, Kathyrn; Thorpe, Andrea S; Menges, Eric S

2013-10-01

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models. © 2013 Society for Conservation Biology.

Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

NASA Astrophysics Data System (ADS)

Khan, Muhammad Yousaf; Mittnik, Stefan

2018-01-01

In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

Hierarchical Architectural Considerations in Econometric Modeling of Manufacturing Systems

DTIC Science & Technology

1981-06-01

behavioral factors must also be considered. A proposed economic model, to be aligned with ICAM program intentions, should be generic and have the... relevant to the effort and to identify contractors, if any, involved in economic model development. Due to the nature of involvement of other con...tractors with the ICAM program office, information which was thought relevant to the initiation of the current effort was in a lag-time and was

Glossary for econometrics and epidemiology.

PubMed

Gunasekara, F Imlach; Carter, K; Blakely, T

2008-10-01

Epidemiologists and econometricians are often interested in similar topics-socioeconomic position and health outcomes-but the different languages that epidemiologists and economists use to interpret and discuss their results can create a barrier to mutual communication. This glossary defines key terms used in econometrics and epidemiology to assist in bridging this gap.

Time Series Econometrics for the 21st Century

ERIC Educational Resources Information Center

Hansen, Bruce E.

2017-01-01

The field of econometrics largely started with time series analysis because many early datasets were time-series macroeconomic data. As the field developed, more cross-sectional and longitudinal datasets were collected, which today dominate the majority of academic empirical research. In nonacademic (private sector, central bank, and governmental)…

Daily air quality index forecasting with hybrid models: A case in China.

PubMed

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-12-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Novel Wind Speed Forecasting Model for Wind Farms of Northwest China

NASA Astrophysics Data System (ADS)

Wang, Jian-Zhou; Wang, Yun

2017-01-01

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon's Signed-Rank test, and Morgan-Granger-Newbold test tell us that the proposed model is different from the compared models.

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

NASA Astrophysics Data System (ADS)

Huang, Xin; Wang, Huaning; Xu, Long; Liu, Jinfu; Li, Rong; Dai, Xinghua

2018-03-01

Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2014-10-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.

Forecasting daily emergency department visits using calendar variables and ambient temperature readings.

PubMed

Marcilio, Izabel; Hajat, Shakoor; Gouveia, Nelson

2013-08-01

This study aimed to develop different models to forecast the daily number of patients seeking emergency department (ED) care in a general hospital according to calendar variables and ambient temperature readings and to compare the models in terms of forecasting accuracy. The authors developed and tested six different models of ED patient visits using total daily counts of patient visits to an ED in Sao Paulo, Brazil, from January 1, 2008, to December 31, 2010. The first 33 months of the data set were used to develop the ED patient visits forecasting models (the training set), leaving the last 3 months to measure each model's forecasting accuracy by the mean absolute percentage error (MAPE). Forecasting models were developed using three different time-series analysis methods: generalized linear models (GLM), generalized estimating equations (GEE), and seasonal autoregressive integrated moving average (SARIMA). For each method, models were explored with and without the effect of mean daily temperature as a predictive variable. The daily mean number of ED visits was 389, ranging from 166 to 613. Data showed a weekly seasonal distribution, with highest patient volumes on Mondays and lowest patient volumes on weekends. There was little variation in daily visits by month. GLM and GEE models showed better forecasting accuracy than SARIMA models. For instance, the MAPEs from GLM models and GEE models at the first month of forecasting (October 2012) were 11.5 and 10.8% (models with and without control for the temperature effect, respectively), while the MAPEs from SARIMA models were 12.8 and 11.7%. For all models, controlling for the effect of temperature resulted in worse or similar forecasting ability than models with calendar variables alone, and forecasting accuracy was better for the short-term horizon (7 days in advance) than for the longer term (30 days in advance). This study indicates that time-series models can be developed to provide forecasts of daily ED patient visits, and forecasting ability was dependent on the type of model employed and the length of the time horizon being predicted. In this setting, GLM and GEE models showed better accuracy than SARIMA models. Including information about ambient temperature in the models did not improve forecasting accuracy. Forecasting models based on calendar variables alone did in general detect patterns of daily variability in ED volume and thus could be used for developing an automated system for better planning of personnel resources. © 2013 by the Society for Academic Emergency Medicine.

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

NASA Astrophysics Data System (ADS)

Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

2017-03-01

Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km  × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

Towards an Australian ensemble streamflow forecasting system for flood prediction and water management

NASA Astrophysics Data System (ADS)

Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.

2016-12-01

Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.

Weighting of NMME temperature and precipitation forecasts across Europe

NASA Astrophysics Data System (ADS)

Slater, Louise J.; Villarini, Gabriele; Bradley, A. Allen

2017-09-01

Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCM) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.

A comparison of GLAS SAT and NMC high resolution NOSAT forecasts from 19 and 11 February 1976

NASA Technical Reports Server (NTRS)

Atlas, R.

1979-01-01

A subjective comparison of the Goddard Laboratory for Atmospheric Sciences (GLAS) and the National Meteorological Center (NMC) high resolution model forecasts is presented. Two cases where NMC's operational model in 1976 had serious difficulties in forecasting for the United States were examined. For each of the cases, the GLAS model forecasts from initial conditions which included satellite sounding data were compared directly to the NMC higher resolution model forecasts, from initial conditions which excluded the satellite data. The comparison showed that the GLAS satellite forecasts significantly improved upon the current NMC operational model's predictions in both cases.

Satellite Sounder Data Assimilation for Improving Alaska Region Weather Forecast

NASA Technical Reports Server (NTRS)

Zhu, Jiang; Stevens, E.; Zhang, X.; Zavodsky, B. T.; Heinrichs, T.; Broderson, D.

2014-01-01

A case study and monthly statistical analysis using sounder data assimilation to improve the Alaska regional weather forecast model are presented. Weather forecast in Alaska faces challenges as well as opportunities. Alaska has a large land with multiple types of topography and coastal area. Weather forecast models must be finely tuned in order to accurately predict weather in Alaska. Being in the high-latitudes provides Alaska greater coverage of polar orbiting satellites for integration into forecasting models than the lower 48. Forecasting marine low stratus clouds is critical to the Alaska aviation and oil industry and is the current focus of the case study. NASA AIRS/CrIS sounder profiles data are used to do data assimilation for the Alaska regional weather forecast model to improve Arctic marine stratus clouds forecast. Choosing physical options for the WRF model is discussed. Preprocess of AIRS/CrIS sounder data for data assimilation is described. Local observation data, satellite data, and global data assimilation data are used to verify and/or evaluate the forecast results by the MET tools Model Evaluation Tools (MET).

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be either an intermediate forecast between the extremes of the ensemble spread or a manually selected forecast based on a meteorologists advice. 2. Downstream catchments with low influence of weather forecast In downstream catchments with strong human impact on discharge (e.g. by reservoir operation) and large influence of upstream gauge observation quality on forecast quality, the 'overall error' may in most cases be larger than the combination of the 'model error' and an ensemble spread. Therefore, the overall forecast uncertainty bounds are calculated differently: a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. Here, additionally the corresponding inflow hydrograph from all upstream catchments must be used. b) As for an upstream catchment, the uncertainty range is determined by combination of 'model error' and the ensemble member forecasts c) In addition, the 'overall error' is superimposed on the 'lead forecast'. For reasons of consistency, the lead forecast must be based on the same meteorological forecast in the downstream and all upstream catchments. d) From the resulting two uncertainty ranges (one from the ensemble forecast and 'model error', one from the 'lead forecast' and 'overall error'), the envelope is taken as the most prudent uncertainty range. In sum, the uncertainty associated with each forecast run is calculated and communicated to the public in the form of 10% and 90% percentiles. As in part I of this study, the methodology as well as the useful- or uselessness of the resulting uncertainty ranges will be presented and discussed by typical examples.

New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.

ERIC Educational Resources Information Center

Song, Qiang; Chissom, Brad S.

Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…

On an improvement of UV index forecast: UV index diagnosis and forecast for Belsk, Poland, in Spring/Summer 1999

NASA Astrophysics Data System (ADS)

Krzyścin, J. W.; Jaroslawski, J.; Sobolewski, P.

2001-10-01

A forecast of the UV index for the following day is presented. The standard approach to the UV index modelling is applied, i.e., the clear-sky UV index is multiplied by the UV cloud transmission factor. The input to the clear-sky model (tropospheric ultraviolet and visible-TUV model, Madronich, in: M. Tevini (Ed.), Environmental Effects of Ultraviolet Radiation, Lewis Publisher, Boca Raton, /1993, p. 17) consists of the total ozone forecast (by a regression model using the observed and forecasted meteorological variables taken as the initial values of aviation (AVN) global model and their 24-hour forecasts, respectively) and aerosols optical depth (AOD) forecast (assumed persistence). The cloud transmission factor forecast is inferred from the 24-h AVN model run for the total (Sun/+sky) solar irradiance at noon. The model is validated comparing the UV index forecasts with the observed values, which are derived from the daily pattern of the UV erythemal irradiance taken at Belsk (52°N,21°E), Poland, by means of the UV Biometer Solar model 501A for the period May-September 1999. Eighty-one percent and 92% of all forecasts fall into /+/-1 and /+/-2 index unit range, respectively. Underestimation of UV index occurs only in 15%. Thus, the model gives a high security in Sun protection for the public. It is found that in /~35% of all cases a more accurate forecast of AOD is needed to estimate the daily maximum of clear-sky irradiance with the error not exceeding 5%. The assumption of the persistence of the cloud characteristics appears as an alternative to the 24-h forecast of the cloud transmission factor in the case when the AVN prognoses are not available.

Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

2016-12-01

The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

Predictive Skill of Meteorological Drought Based on Multi-Model Ensemble Forecasts: A Real-Time Assessment

NASA Astrophysics Data System (ADS)

Chen, L. C.; Mo, K. C.; Zhang, Q.; Huang, J.

2014-12-01

Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Starting in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the North American Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the predictive skill of meteorological drought using real-time NMME forecasts for the period from May 2012 to May 2014. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation coefficient and root-mean-square errors against the observations, are used to evaluate forecast skill.Similar to the assessment based on NMME retrospective forecasts, predictive skill of monthly-mean precipitation (P) forecasts is generally low after the second month and errors vary among models. Although P forecast skill is not large, SPI predictive skill is high and the differences among models are small. The skill mainly comes from the P observations appended to the model forecasts. This factor also contributes to the similarity of SPI prediction among the six models. Still, NMME SPI ensemble forecasts have higher skill than those based on individual models or persistence, and the 6-month SPI forecasts are skillful out to four months. The three major drought events occurred during the 2012-2014 period, the 2012 Central Great Plains drought, the 2013 Upper Midwest flash drought, and 2013-2014 California drought, are used as examples to illustrate the system's strength and limitations. For precipitation-driven drought events, such as the 2012 Central Great Plains drought, NMME SPI forecasts perform well in predicting drought severity and spatial patterns. For fast-developing drought events, such as the 2013 Upper Midwest flash drought, the system failed to capture the onset of the drought.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

An investigation on the determinants of carbon emissions for OECD countries: empirical evidence from panel models robust to heterogeneity and cross-sectional dependence.

PubMed

Dogan, Eyup; Seker, Fahri

2016-07-01

This empirical study analyzes the impacts of real income, energy consumption, financial development and trade openness on CO2 emissions for the OECD countries in the Environmental Kuznets Curve (EKC) model by using panel econometric approaches that consider issues of heterogeneity and cross-sectional dependence. Results from the Pesaran CD test, the Pesaran-Yamagata's homogeneity test, the CADF and the CIPS unit root tests, the LM bootstrap cointegration test, the DSUR estimator, and the Emirmahmutoglu-Kose Granger causality test indicate that (i) the panel time-series data are heterogeneous and cross-sectionally dependent; (ii) CO2 emissions, real income, the quadratic income, energy consumption, financial development and openness are integrated of order one; (iii) the analyzed data are cointegrated; (iv) the EKC hypothesis is validated for the OECD countries; (v) increases in openness and financial development mitigate the level of emissions whereas energy consumption contributes to carbon emissions; (vi) a variety of Granger causal relationship is detected among the analyzed variables; and (vii) empirical results and policy recommendations are accurate and efficient since panel econometric models used in this study account for heterogeneity and cross-sectional dependence in their estimation procedures.

Next-Day Earthquake Forecasts for California

NASA Astrophysics Data System (ADS)

Werner, M. J.; Jackson, D. D.; Kagan, Y. Y.

2008-12-01

We implemented a daily forecast of m > 4 earthquakes for California in the format suitable for testing in community-based earthquake predictability experiments: Regional Earthquake Likelihood Models (RELM) and the Collaboratory for the Study of Earthquake Predictability (CSEP). The forecast is based on near-real time earthquake reports from the ANSS catalog above magnitude 2 and will be available online. The model used to generate the forecasts is based on the Epidemic-Type Earthquake Sequence (ETES) model, a stochastic model of clustered and triggered seismicity. Our particular implementation is based on the earlier work of Helmstetter et al. (2006, 2007), but we extended the forecast to all of Cali-fornia, use more data to calibrate the model and its parameters, and made some modifications. Our forecasts will compete against the Short-Term Earthquake Probabilities (STEP) forecasts of Gersten-berger et al. (2005) and other models in the next-day testing class of the CSEP experiment in California. We illustrate our forecasts with examples and discuss preliminary results.

Using Temperature Forecasts to Improve Seasonal Streamflow Forecasts in the Colorado and Rio Grande Basins

NASA Astrophysics Data System (ADS)

Lehner, F.; Wood, A.; Llewellyn, D.; Blatchford, D. B.; Goodbody, A. G.; Pappenberger, F.

2017-12-01

Recent studies have documented the influence of increasing temperature on streamflow across the American West, including snow-melt driven rivers such as the Colorado or Rio Grande. At the same time, some basins are reporting decreasing skill in seasonal streamflow forecasts, termed water supply forecasts (WSFs), over the recent decade. While the skill in seasonal precipitation forecasts from dynamical models remains low, their skill in predicting seasonal temperature variations could potentially be harvested for WSFs to account for non-stationarity in regional temperatures. Here, we investigate whether WSF skill can be improved by incorporating seasonal temperature forecasts from dynamical forecasting models (from the North American Multi Model Ensemble and the European Centre for Medium-Range Weather Forecast System 4) into traditional statistical forecast models. We find improved streamflow forecast skill relative to traditional WSF approaches in a majority of headwater locations in the Colorado and Rio Grande basins. Incorporation of temperature into WSFs thus provides a promising avenue to increase the robustness of current forecasting techniques in the face of continued regional warming.

An enhanced PM 2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

NASA Astrophysics Data System (ADS)

Cobourn, W. Geoffrey

2010-08-01

An enhanced PM 2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM 2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM 2.5 air quality is more likely to be critical for human health. The enhanced PM 2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM 2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM 2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, WanYin; Zhang, Jie; Florita, Anthony

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance,more » cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.« less

Forecast first: An argument for groundwater modeling in reverse

USGS Publications Warehouse

White, Jeremy

2017-01-01

Numerical groundwater models are important compo-nents of groundwater analyses that are used for makingcritical decisions related to the management of ground-water resources. In this support role, models are oftenconstructed to serve a specific purpose that is to provideinsights, through simulation, related to a specific func-tion of a complex aquifer system that cannot be observeddirectly (Anderson et al. 2015).For any given modeling analysis, several modelinput datasets must be prepared. Herein, the datasetsrequired to simulate the historical conditions are referredto as the calibration model, and the datasets requiredto simulate the model’s purpose are referred to as theforecast model. Future groundwater conditions or otherunobserved aspects of the groundwater system may besimulated by the forecast model—the outputs of interestfrom the forecast model represent the purpose of themodeling analysis. Unfortunately, the forecast model,needed to simulate the purpose of the modeling analysis,is seemingly an afterthought—calibration is where themajority of time and effort are expended and calibrationis usually completed before the forecast model is evenconstructed. Herein, I am proposing a new groundwatermodeling workflow, referred to as the “forecast first”workflow, where the forecast model is constructed at anearlier stage in the modeling analysis and the outputsof interest from the forecast model are evaluated duringsubsequent tasks in the workflow.

Robustness of disaggregate oil and gas discovery forecasting models

USGS Publications Warehouse

Attanasi, E.D.; Schuenemeyer, J.H.

1989-01-01

The trend in forecasting oil and gas discoveries has been to develop and use models that allow forecasts of the size distribution of future discoveries. From such forecasts, exploration and development costs can more readily be computed. Two classes of these forecasting models are the Arps-Roberts type models and the 'creaming method' models. This paper examines the robustness of the forecasts made by these models when the historical data on which the models are based have been subject to economic upheavals or when historical discovery data are aggregated from areas having widely differing economic structures. Model performance is examined in the context of forecasting discoveries for offshore Texas State and Federal areas. The analysis shows how the model forecasts are limited by information contained in the historical discovery data. Because the Arps-Roberts type models require more regularity in discovery sequence than the creaming models, prior information had to be introduced into the Arps-Roberts models to accommodate the influence of economic changes. The creaming methods captured the overall decline in discovery size but did not easily allow introduction of exogenous information to compensate for incomplete historical data. Moreover, the predictive log normal distribution associated with the creaming model methods appears to understate the importance of the potential contribution of small fields. ?? 1989.

Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity

NASA Astrophysics Data System (ADS)

Szolgayová, Elena Peksová; Danačová, Michaela; Komorniková, Magda; Szolgay, Ján

2017-06-01

It is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-type nonlinear time series models are considered here. The fitting, forecasting and simulation performance of such models have to be explored on a case-by-case basis. The goal of this paper is to test and develop an appropriate methodology for model fitting and forecasting applicable for daily river discharge forecast error data from the GARCH family of time series models. We concentrated on verifying whether the use of a GARCH-type model is suitable for modelling and forecasting a hydrological model error time series on the Hron and Morava Rivers in Slovakia. For this purpose we verified the presence of heteroscedasticity in the simulation error series of the KLN multilinear flow routing model; then we fitted the GARCH-type models to the data and compared their fit with that of an ARMA - type model. We produced one-stepahead forecasts from the fitted models and again provided comparisons of the model's performance.

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

NASA Astrophysics Data System (ADS)

Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

2017-09-01

Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

Evaluation of factors affecting prescribing behaviors, in iran pharmaceutical market by econometric methods.

PubMed

Tahmasebi, Nima; Kebriaeezadeh, Abbas

2015-01-01

Prescribing behavior of physicians affected by many factors. The present study is aimed at discovering the simultaneous effects of the evaluated factors (including: price, promotion and demographic characteristics of physicians) and quantification of these effects. In order to estimate these effects, Fluvoxamine (an antidepressant drug) was selected and the model was figured out by panel data method in econometrics. We found that insurance and advertisement respectively are the most effective on increasing the frequency of prescribing, whilst negative correlation was observed between price and the frequency of prescribing a drug. Also brand type is more sensitive to negative effect of price than to generic. Furthermore, demand for a prescription drug is related with physician demographics (age and sex). According to the results of this study, pharmaceutical companies should pay more attention to the demographic characteristics of physicians (age and sex) and their advertisement and pricing strategies.

Evaluation of Factors Affecting Prescribing Behaviors, in Iran Pharmaceutical Market by Econometric Methods

PubMed Central

Tahmasebi, Nima; Kebriaeezadeh, Abbas

2015-01-01

Prescribing behavior of physicians affected by many factors. The present study is aimed at discovering the simultaneous effects of the evaluated factors (including: price, promotion and demographic characteristics of physicians) and quantification of these effects. In order to estimate these effects, Fluvoxamine (an antidepressant drug) was selected and the model was figured out by panel data method in econometrics. We found that insurance and advertisement respectively are the most effective on increasing the frequency of prescribing, whilst negative correlation was observed between price and the frequency of prescribing a drug. Also brand type is more sensitive to negative effect of price than to generic. Furthermore, demand for a prescription drug is related with physician demographics (age and sex). According to the results of this study, pharmaceutical companies should pay more attention to the demographic characteristics of physicians (age and sex) and their advertisement and pricing strategies. PMID:25901174

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

An econometric model of the U.S. secondary copper industry: Recycling versus disposal

USGS Publications Warehouse

Slade, M.E.

1980-01-01

In this paper, a theoretical model of secondary recovery is developed that integrates microeconomic theories of production and cost with a dynamic model of scrap generation and accumulation. The model equations are estimated for the U.S. secondary copper industry and used to assess the impacts that various policies and future events have on copper recycling rates. The alternatives considered are: subsidies for secondary production, differing energy costs, and varying ore quality in primary production. ?? 1990.

Modified Maxium Likelihood Estimation Method for Completely Separated and Quasi-Completely Separated Data for a Dose-Response Model

DTIC Science & Technology

2015-08-01

McCullagh, P.; Nelder, J.A. Generalized Linear Model , 2nd ed.; Chapman and Hall: London, 1989. 7. Johnston, J. Econometric Methods, 3rd ed.; McGraw...FOR A DOSE-RESPONSE MODEL ECBC-TN-068 Kyong H. Park Steven J. Lagan RESEARCH AND TECHNOLOGY DIRECTORATE August 2015 Approved for public release...Likelihood Estimation Method for Completely Separated and Quasi-Completely Separated Data for a Dose-Response Model 5a. CONTRACT NUMBER 5b. GRANT

A joint econometric analysis of seat belt use and crash-related injury severity.

PubMed

Eluru, Naveen; Bhat, Chandra R

2007-09-01

This paper formulates a comprehensive econometric structure that recognizes two important issues in crash-related injury severity analysis. First, the impact of a factor on injury severity may be moderated by various observed and unobserved variables specific to an individual or to a crash. Second, seat belt use is likely to be endogenous to injury severity. That is, it is possible that intrinsically unsafe drivers do not wear seat belts and are the ones likely to be involved in high injury severity crashes because of their unsafe driving habits. The preceding issues are considered in the current research effort through the development of a comprehensive model of seat belt use and injury severity that takes the form of a joint correlated random coefficients binary-ordered response system. To our knowledge, this is the first instance of such a model formulation and application not only in the safety analysis literature, but in the econometrics literature in general. The empirical analysis is based on the 2003 General Estimates System (GES) data base. Several types of variables are considered to explain seat belt use and injury severity levels, including driver characteristics, vehicle characteristics, roadway design attributes, environmental factors, and crash characteristics. The results, in addition to confirming the effects of various explanatory variables, also highlight the importance of (a) considering the moderating effects of unobserved individual/crash-related factors on the determinants of injury severity and (b) seat belt use endogeneity. From a policy standpoint, the results suggest that seat belt non-users, when apprehended in the act, should perhaps be subjected to both a fine (to increase the chances that they wear seat belts) as well as mandatory enrollment in a defensive driving course (to attempt to change their aggressive driving behaviors).

Technical Change in the North American Forestry Sector: A Review

Treesearch

Jeffery C. Stier; David N. Bengston

1992-01-01

Economists have examined the impact of technical change on the forest products sector using the historical, index number, and econometric approaches. This paper reviews econometric analyses of the rate and bias of technical change, examining functional form, factors included, and empirical results. Studies are classified as first- second-, or third-generation...

Econometric Methods for Causal Evaluation of Education Policies and Practices: A Non-Technical Guide

ERIC Educational Resources Information Center

Schlotter, Martin; Schwerdt, Guido; Woessmann, Ludger

2011-01-01

Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and intuition…

The Status of Econometrics in the Economics Major: A Survey

ERIC Educational Resources Information Center

Johnson, Bruce K.; Perry, John J.; Petkus, Marie

2012-01-01

In this article, the authors describe the place of econometrics in undergraduate economics curricula in all American colleges and universities that offer economics majors as listed in the "U.S. News & World Report" "Best Colleges 2010" guide ("U.S. News & World Report" 2009). Data come from online catalogs, departmental Web sites, and online…

Gaming via Computer Simulation Techniques for Junior College Economics Education. Final Report.

ERIC Educational Resources Information Center

Thompson, Fred A.

A study designed to answer the need for more attractive and effective economics education involved the teaching of one junior college economics class by the conventional (lecture) method and an experimental class by computer simulation techniques. Econometric models approximating the "real world" were computer programed to enable the experimental…

An Econometric Model of the Scholastic Aptitude Test Performance of State Educational Systems.

ERIC Educational Resources Information Center

Hashway, Robert M.; And Others

1991-01-01

Nationwide data were partitioned into wealth, fiscal policy, fiscal orientation, and Scholastic Aptitude Test (SAT) performance and participation. Largest between-group differences show that low SAT achieving states have a larger percentage of seniors taking the SAT, along with higher per capita income, per pupil expenditures, and teacher…

College Choice in America.

ERIC Educational Resources Information Center

Manski, Charles F.; And Others

The processes of choosing a college and being accepted by a college are analyzed, based on data on nearly 23,000 seniors from more than 1,300 high schools from the National Longitudinal Study of the Class of 1972. Econometric modeling and descriptive statistics are provided on: student behavior in selecting a college, choosing school/nonschool…

The effect of personal experience on choice-based preferences for wildfire protection programs

Treesearch

Tom Holmes; Armando Gonzalez-Caban; John Loomis; Jose Sanchez

2013-01-01

In this paper, we investigate homeowner preferences and willingness to pay for wildfire protection programs using a choice experiment with three attributes: risk, loss and cost. Preference heterogeneity among survey respondents was examined using three econometric models and risk preferences were evaluated by comparing willingness to pay for wildfire protection...

School Cost Functions: A Meta-Regression Analysis

ERIC Educational Resources Information Center

Colegrave, Andrew D.; Giles, Margaret J.

2008-01-01

The education cost literature includes econometric studies attempting to determine economies of scale, or estimate an optimal school or district size. Not only do their results differ, but the studies use dissimilar data, techniques, and models. To derive value from these studies requires that the estimates be made comparable. One method to do…

Examining the Earnings Trajectories of Community College Students Using a Piecewise Growth Curve Modeling Approach

ERIC Educational Resources Information Center

Jaggars, Shanna Smith; Xu, Di

2016-01-01

Policymakers have become increasingly concerned with measuring--and holding colleges accountable for--students' labor market outcomes. In this article we introduce a piecewise growth curve approach to analyzing community college students' labor market outcomes, and we discuss how this approach differs from two popular econometric approaches:…

TRACKING THE EMISSION OF CARBON DIOXIDE BY NATION, SECTOR, AND FUEL TYPE: A TRACE GAS ACCOUNTING SYSTEM (TGAS)

EPA Science Inventory

The paper describes a new way to estimate an efficient econometric model of global emissions of carbon dioxide (CO2) by nation, sector, and fuel type. Equations for fuel intensity are estimated for coal, oil, natural gas, electricity, and heat for six sectors: agricultural, indus...

Economic factors influencing land use changes in the South-Central United States

Treesearch

Ralph J. Alig; Fred C. White; Brian C. Murray

1988-01-01

Econometric models of land use change were estimated for two physiographic regions in the South-Central United States. Results are consistent-with the economic hierarchy of land use, with population and personal income being significant explanatory variables. Findings regarding the importance of relative agricultural and forestry market-based incomes in influencing...

Influences on Labor Market Outcomes of African American College Graduates: A National Study

ERIC Educational Resources Information Center

Strayhorn, Terrell L.

2008-01-01

Using an expanded econometric model, this study sought to estimate more precisely the net effect of independent variables (i.e., attending an HBCU) on three measures of labor market outcomes for African American college graduates. Findings reveal a statistically significant, albeit moderate, relationship between measures of background, human and…

Child Care and the Labor Supply of Married Women: Reduced Form Evidence.

ERIC Educational Resources Information Center

Ribar, David C.

1992-01-01

With data from the Survey of Income Program Participation, a three-equation, reduced-form econometric model is used to generate estimates revealing that the cost of market child care decreases the labor force participation of married women. High wages increase likelihood of working and use of paid child care. (SK)

The Effect of Income Taxation on Labor Supply in the United States.

ERIC Educational Resources Information Center

Triest, Robert K.

1990-01-01

A study used an econometric model to examine the effect of income taxation on labor supply of married women and men. Male labor supply was found to be relatively invariant to income. Impact on married women depended upon the method used to estimate the labor supply function. (SK)

A new scoring method for evaluating the performance of earthquake forecasts and predictions

NASA Astrophysics Data System (ADS)

Zhuang, J.

2009-12-01

This study presents a new method, namely the gambling score, for scoring the performance of earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. A fair scoring scheme should reward the success in a way that is compatible with the risk taken. Suppose that we have the reference model, usually the Poisson model for usual cases or Omori-Utsu formula for the case of forecasting aftershocks, which gives probability p0 that at least 1 event occurs in a given space-time-magnitude window. The forecaster, similar to a gambler, who starts with a certain number of reputation points, bets 1 reputation point on ``Yes'' or ``No'' according to his forecast, or bets nothing if he performs a NA-prediction. If the forecaster bets 1 reputation point of his reputations on ``Yes" and loses, the number of his reputation points is reduced by 1; if his forecasts is successful, he should be rewarded (1-p0)/p0 reputation points. The quantity (1-p0)/p0 is the return (reward/bet) ratio for bets on ``Yes''. In this way, if the reference model is correct, the expected return that he gains from this bet is 0. This rule also applies to probability forecasts. Suppose that p is the occurrence probability of an earthquake given by the forecaster. We can regard the forecaster as splitting 1 reputation point by betting p on ``Yes'' and 1-p on ``No''. In this way, the forecaster's expected pay-off based on the reference model is still 0. From the viewpoints of both the reference model and the forecaster, the rule for rewarding and punishment is fair. This method is also extended to the continuous case of point process models, where the reputation points bet by the forecaster become a continuous mass on the space-time-magnitude range of interest. We also calculate the upper bound of the gambling score when the true model is a renewal process, the stress release model or the ETAS model and when the reference model is the Poisson model.

Ranking product aspects through sentiment analysis of online reviews

NASA Astrophysics Data System (ADS)

Wang, Wei; Wang, Hongwei; Song, Yuan

2017-03-01

The electronic word-of-mouth (e-WOM) is one of the most important among all the factors affecting consumers' behaviours. Opinions towards a product through online reviews will influence purchase decisions of other online consumers by changing their perceptions on the product quality. Furthermore, each product aspect may impact consumers' intentions differently. Thus, sentiment analysis and econometric models are incorporated to examine the relationship between purchase intentions and aspect-opinion pairs, which enable the weight estimation for each product aspect. We first identify product aspects and reduce dimensions to extract aspect-opinion pairs. Next the information gain is calculated for each aspect through entropy theory. Based on sentiment polarity and sentiment strength, we formulate an econometric model by integrating the information gain to measure the aspect's weight. In the experiment, we track 386 digital cameras on Amazon for 39 months, and results show that the aspect weight for digital cameras is detected more precisely than TF-ID and HAC algorithms. The results will bridge product aspects and consumption intention to facilitate e-WOM-based marketing.

Econometric studies of urban population density: a survey.

PubMed

Mcdonald, J F

1989-01-01

This paper presents the 1st reasonably comprehensive survey of empirical research of urban population densities since the publication of the book by Edmonston in 1975. The survey summarizes contributions to empirical knowledge that have been made since 1975 and points toward possible areas for additional research. The paper also provides a brief interpretative intellectual history of the topic. It begins with a personal overview of research in the field. The next section discusses econometric issues that arise in the estimation of population density functions in which density is a function only of a distance to the central business district of the urban area. Section 4 summarizes the studies of a single urban area that went beyond the estimation of simple distance-density functions, and Section 5 discusses studies that sought to explain the variations across urban areas in population density patterns. McDonald refers to the standard theory of urban population density throughout the paper. This basic model is presented in the textbook by Mills and Hamilton and it is assumed that the reader is familiar with the model.

Spatial econometric model of natural disaster impacts on human migration in vulnerable regions of Mexico.

PubMed

Saldaña-Zorrilla, Sergio O; Sandberg, Krister

2009-10-01

Mexico's vast human and environmental diversity offers an initial framework for comprehending some of the prevailing great disparities between rich and poor. Its socio-economic constructed vulnerability to climatic events serves to expand this understanding. Based on a spatial econometric model, this paper tests the contribution of natural disasters to stimulating the emigration process in vulnerable regions of Mexico. Besides coping and adaptive capacity, it assesses the effects of economic losses due to disasters as well as the adverse production and trade conditions of the 1990s on emigration rates in 2000 at the municipality level. Weather-related disasters were responsible for approximately 80 per cent of economic losses in Mexico between 1980 and 2005, mostly in the agricultural sector, which continues to dominate many parts of the country. It is dramatic that this sector generates around only four per cent of gross domestic product but provides a livelihood to about one-quarter of the national population. It is no wonder, therefore, that most emigration from this country arises in vulnerable rural areas.

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2015-08-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

Evaluation and Applications of the Prediction of Intensity Model Error (PRIME) Model

NASA Astrophysics Data System (ADS)

Bhatia, K. T.; Nolan, D. S.; Demaria, M.; Schumacher, A.

2015-12-01

Forecasters and end users of tropical cyclone (TC) intensity forecasts would greatly benefit from a reliable expectation of model error to counteract the lack of consistency in TC intensity forecast performance. As a first step towards producing error predictions to accompany each TC intensity forecast, Bhatia and Nolan (2013) studied the relationship between synoptic parameters, TC attributes, and forecast errors. In this study, we build on previous results of Bhatia and Nolan (2013) by testing the ability of the Prediction of Intensity Model Error (PRIME) model to forecast the absolute error and bias of four leading intensity models available for guidance in the Atlantic basin. PRIME forecasts are independently evaluated at each 12-hour interval from 12 to 120 hours during the 2007-2014 Atlantic hurricane seasons. The absolute error and bias predictions of PRIME are compared to their respective climatologies to determine their skill. In addition to these results, we will present the performance of the operational version of PRIME run during the 2015 hurricane season. PRIME verification results show that it can reliably anticipate situations where particular models excel, and therefore could lead to a more informed protocol for hurricane evacuations and storm preparations. These positive conclusions suggest that PRIME forecasts also have the potential to lower the error in the original intensity forecasts of each model. As a result, two techniques are proposed to develop a post-processing procedure for a multimodel ensemble based on PRIME. The first approach is to inverse-weight models using PRIME absolute error predictions (higher predicted absolute error corresponds to lower weights). The second multimodel ensemble applies PRIME bias predictions to each model's intensity forecast and the mean of the corrected models is evaluated. The forecasts of both of these experimental ensembles are compared to those of the equal-weight ICON ensemble, which currently provides the most reliable forecasts in the Atlantic basin.

MMAB Sea Ice Forecast Page

Science.gov Websites

verification statistics Grumbine, R. W., Virtual Floe Ice Drift Forecast Model Intercomparison, Weather and Forecasting, 13, 886-890, 1998. MMAB Note: Virtual Floe Ice Drift Forecast Model Intercomparison 1996 pdf ~47

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China

PubMed Central

Liu, Dong-jun; Li, Li

2015-01-01

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field. PMID:26110332

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China.

PubMed

Liu, Dong-jun; Li, Li

2015-06-23

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field.

Stochastic Model of Seasonal Runoff Forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, Roman; Watada, Leslie M.

1986-03-01

Each year the National Weather Service and the Soil Conservation Service issue a monthly sequence of five (or six) categorical forecasts of the seasonal snowmelt runoff volume. To describe uncertainties in these forecasts for the purposes of optimal decision making, a stochastic model is formulated. It is a discrete-time, finite, continuous-space, nonstationary Markov process. Posterior densities of the actual runoff conditional upon a forecast, and transition densities of forecasts are obtained from a Bayesian information processor. Parametric densities are derived for the process with a normal prior density of the runoff and a linear model of the forecast error. The structure of the model and the estimation procedure are motivated by analyses of forecast records from five stations in the Snake River basin, from the period 1971-1983. The advantages of supplementing the current forecasting scheme with a Bayesian analysis are discussed.

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE PAGES

Yoo, Wucherl; Sim, Alex

2016-06-24

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Model Error Estimation for the CPTEC Eta Model

NASA Technical Reports Server (NTRS)

Tippett, Michael K.; daSilva, Arlindo

1999-01-01

Statistical data assimilation systems require the specification of forecast and observation error statistics. Forecast error is due to model imperfections and differences between the initial condition and the actual state of the atmosphere. Practical four-dimensional variational (4D-Var) methods try to fit the forecast state to the observations and assume that the model error is negligible. Here with a number of simplifying assumption, a framework is developed for isolating the model error given the forecast error at two lead-times. Two definitions are proposed for the Talagrand ratio tau, the fraction of the forecast error due to model error rather than initial condition error. Data from the CPTEC Eta Model running operationally over South America are used to calculate forecast error statistics and lower bounds for tau.

Research on Nonlinear Time Series Forecasting of Time-Delay NN Embedded with Bayesian Regularization

NASA Astrophysics Data System (ADS)

Jiang, Weijin; Xu, Yusheng; Xu, Yuhui; Wang, Jianmin

Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization. Furthermore, the model is applied to forecast the imp&exp trades in one industry. The results showed that the improved model has excellent generalization capabilities, which not only learned the historical curve, but efficiently predicted the trend of business. Comparing with common evaluation of forecasts, we put on a conclusion that nonlinear forecast can not only focus on data combination and precision improvement, it also can vividly reflect the nonlinear characteristic of the forecasting system. While analyzing the forecasting precision of the model, we give a model judgment by calculating the nonlinear characteristic value of the combined serial and original serial, proved that the forecasting model can reasonably 'catch' the dynamic characteristic of the nonlinear system which produced the origin serial.

A scoping review of malaria forecasting: past work and future directions

PubMed Central

Zinszer, Kate; Verma, Aman D; Charland, Katia; Brewer, Timothy F; Brownstein, John S; Sun, Zhuoyu; Buckeridge, David L

2012-01-01

Objectives There is a growing body of literature on malaria forecasting methods and the objective of our review is to identify and assess methods, including predictors, used to forecast malaria. Design Scoping review. Two independent reviewers searched information sources, assessed studies for inclusion and extracted data from each study. Information sources Search strategies were developed and the following databases were searched: CAB Abstracts, EMBASE, Global Health, MEDLINE, ProQuest Dissertations & Theses and Web of Science. Key journals and websites were also manually searched. Eligibility criteria for included studies We included studies that forecasted incidence, prevalence or epidemics of malaria over time. A description of the forecasting model and an assessment of the forecast accuracy of the model were requirements for inclusion. Studies were restricted to human populations and to autochthonous transmission settings. Results We identified 29 different studies that met our inclusion criteria for this review. The forecasting approaches included statistical modelling, mathematical modelling and machine learning methods. Climate-related predictors were used consistently in forecasting models, with the most common predictors being rainfall, relative humidity, temperature and the normalised difference vegetation index. Model evaluation was typically based on a reserved portion of data and accuracy was measured in a variety of ways including mean-squared error and correlation coefficients. We could not compare the forecast accuracy of models from the different studies as the evaluation measures differed across the studies. Conclusions Applying different forecasting methods to the same data, exploring the predictive ability of non-environmental variables, including transmission reducing interventions and using common forecast accuracy measures will allow malaria researchers to compare and improve models and methods, which should improve the quality of malaria forecasting. PMID:23180505

Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA.

PubMed

Bildirici, Melike; Ersin, Özgür Ömer

2018-01-01

The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO 2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO 2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO 2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

2012-01-01

Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

A study for systematic errors of the GLA forecast model in tropical regions

NASA Technical Reports Server (NTRS)

Chen, Tsing-Chang; Baker, Wayman E.; Pfaendtner, James; Corrigan, Martin

1988-01-01

From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system.

Long-range correlations in time series generated by time-fractional diffusion: A numerical study

NASA Astrophysics Data System (ADS)

Barbieri, Davide; Vivoli, Alessandro

2005-09-01

Time series models showing power law tails in autocorrelation functions are common in econometrics. A special non-Markovian model for such kind of time series is provided by the random walk introduced by Gorenflo et al. as a discretization of time fractional diffusion. The time series so obtained are analyzed here from a numerical point of view in terms of autocorrelations and covariance matrices.

The future of housing in the United States: an econometric model of long-term predictions for the 2000 RPA timber assessment.

Treesearch

Claire A. Montgomery

2001-01-01

This report presents historical trends and future projections of forest, agricultural, and urban and other land uses for the South-Central United States. A land use share model is used to investigate the relation between the areas of land in alternative uses and economic and demographic factors influencing land use decisions. Two different versions of the empirical...

Climate change and future land use in the United States: an economic approach

Treesearch

David Haim; Ralph J. Alig; Andrew J. Plantinga; Brent Sohngen

2011-01-01

An econometric land-use model is used to project regional and national land-use changes in the United States under two IPCC emissions scenarios. The key driver of land-use change in the model is county-level measures of net returns to five major land uses. The net returns are modified for the IPCC scenarios according to assumed trends in population and income and...

Oscillatory dynamics of investment and capacity utilization

NASA Astrophysics Data System (ADS)

Greenblatt, R. E.

2017-01-01

Capitalist economic systems display a wide variety of oscillatory phenomena whose underlying causes are often not well understood. In this paper, I consider a very simple model of the reciprocal interaction between investment, capacity utilization, and their time derivatives. The model, which gives rise periodic oscillations, predicts qualitatively the phase relations between these variables. These predictions are observed to be consistent in a statistical sense with econometric data from the US economy.

An Integrated Enrollment Forecast Model. IR Applications, Volume 15, January 18, 2008

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2008-01-01

Enrollment forecasting is the central component of effective budget and program planning. The integrated enrollment forecast model is developed to achieve a better understanding of the variables affecting student enrollment and, ultimately, to perform accurate forecasts. The transfer function model of the autoregressive integrated moving average…

Can we use Earth Observations to improve monthly water level forecasts?

NASA Astrophysics Data System (ADS)

Slater, L. J.; Villarini, G.

2017-12-01

Dynamical-statistical hydrologic forecasting approaches benefit from different strengths in comparison with traditional hydrologic forecasting systems: they are computationally efficient, can integrate and `learn' from a broad selection of input data (e.g., General Circulation Model (GCM) forecasts, Earth Observation time series, teleconnection patterns), and can take advantage of recent progress in machine learning (e.g. multi-model blending, post-processing and ensembling techniques). Recent efforts to develop a dynamical-statistical ensemble approach for forecasting seasonal streamflow using both GCM forecasts and changing land cover have shown promising results over the U.S. Midwest. Here, we use climate forecasts from several GCMs of the North American Multi Model Ensemble (NMME) alongside 15-minute stage time series from the National River Flow Archive (NRFA) and land cover classes extracted from the European Space Agency's Climate Change Initiative 300 m annual Global Land Cover time series. With these data, we conduct systematic long-range probabilistic forecasting of monthly water levels in UK catchments over timescales ranging from one to twelve months ahead. We evaluate the improvement in model fit and model forecasting skill that comes from using land cover classes as predictors in the models. This work opens up new possibilities for combining Earth Observation time series with GCM forecasts to predict a variety of hazards from space using data science techniques.

A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

NASA Astrophysics Data System (ADS)

Schepen, Andrew; Zhao, Tongtiegang; Wang, Quan J.; Robertson, David E.

2018-03-01

Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

A stochastic post-processing method for solar irradiance forecasts derived from NWPs models

NASA Astrophysics Data System (ADS)

Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.

2010-09-01

Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.

Forecasting daily patient volumes in the emergency department.

PubMed

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

New product forecasting with limited or no data

NASA Astrophysics Data System (ADS)

Ismai, Zuhaimy; Abu, Noratikah; Sufahani, Suliadi

2016-10-01

In the real world, forecasts would always be based on historical data with the assumption that the behaviour be the same for the future. But how do we forecast when there is no such data available? New product or new technologies normally has limited amount of data available. Knowing that forecasting is valuable for decision making, this paper presents forecasting of new product or new technologies using aggregate diffusion models and modified Bass Model. A newly launched Proton car and its penetration was chosen to demonstrate the possibility of forecasting sales demand where there is limited or no data available. The model was developed to forecast diffusion of new vehicle or an innovation in the Malaysian society. It is to represent the level of spread on the new vehicle among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new product. This model will forecast the car sales volume. A procedure of the proposed diffusion model was designed and the parameters were estimated. Results obtained by applying the proposed diffusion model and numerical calculation shows that the model is robust and effective for forecasting demand of the new vehicle. The results reveal that newly developed modified Bass diffusion of demand function has significantly contributed for forecasting the diffusion of new Proton car or new product.

Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Steinsland, Ingelin

2014-05-01

This study introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times, and investigates criterions for evaluation of multi-variate forecasts. A post-processing approach is used, and a Gaussian model is applied for transformed variables. The post processing model has two main components, the mean model and the dependency model. The mean model is used to estimate the marginal distributions for forecasted inflow for each catchment and lead time, whereas the dependency models was used to estimate the full multivariate distribution of forecasts, i.e. co-variances between catchments and lead times. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. Two criterions were used for evaluating the added value of the dependency model. The first one was the Energy score (ES) that is a multi-dimensional generalization of continuous rank probability score (CRPS). ES was calculated for all lead-times and catchments together, for each catchment across all lead times and for each lead time across all catchments. The second criterion was to use CRPS for forecasted inflows accumulated over several lead times and catchments. The results showed that ES was not very sensitive to correct covariance structure, whereas CRPS for accumulated flows where more suitable for evaluating the dependency model. This indicates that it is more appropriate to evaluate relevant univariate variables that depends on the dependency structure then to evaluate the multivariate forecast directly.

Statistical post-processing of seasonal multi-model forecasts: Why is it so hard to beat the multi-model mean?

NASA Astrophysics Data System (ADS)

Siegert, Stefan

2017-04-01

Initialised climate forecasts on seasonal time scales, run several months or even years ahead, are now an integral part of the battery of products offered by climate services world-wide. The availability of seasonal climate forecasts from various modeling centres gives rise to multi-model ensemble forecasts. Post-processing such seasonal-to-decadal multi-model forecasts is challenging 1) because the cross-correlation structure between multiple models and observations can be complicated, 2) because the amount of training data to fit the post-processing parameters is very limited, and 3) because the forecast skill of numerical models tends to be low on seasonal time scales. In this talk I will review new statistical post-processing frameworks for multi-model ensembles. I will focus particularly on Bayesian hierarchical modelling approaches, which are flexible enough to capture commonly made assumptions about collective and model-specific biases of multi-model ensembles. Despite the advances in statistical methodology, it turns out to be very difficult to out-perform the simplest post-processing method, which just recalibrates the multi-model ensemble mean by linear regression. I will discuss reasons for this, which are closely linked to the specific characteristics of seasonal multi-model forecasts. I explore possible directions for improvements, for example using informative priors on the post-processing parameters, and jointly modelling forecasts and observations.

Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

PubMed

Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

2016-02-24

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles.

PubMed

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

PubMed Central

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words. PMID:27313605

Stationarity test with a direct test for heteroskedasticity in exchange rate forecasting models

NASA Astrophysics Data System (ADS)

Khin, Aye Aye; Chau, Wong Hong; Seong, Lim Chee; Bin, Raymond Ling Leh; Teng, Kevin Low Lock

2017-05-01

Global economic has been decreasing in the recent years, manifested by the greater exchange rates volatility on international commodity market. This study attempts to analyze some prominent exchange rate forecasting models on Malaysian commodity trading: univariate ARIMA, ARCH and GARCH models in conjunction with stationarity test on residual diagnosis direct testing of heteroskedasticity. All forecasting models utilized the monthly data from 1990 to 2015. Given a total of 312 observations, the data used to forecast both short-term and long-term exchange rate. The forecasting power statistics suggested that the forecasting performance of ARIMA (1, 1, 1) model is more efficient than the ARCH (1) and GARCH (1, 1) models. For ex-post forecast, exchange rate was increased from RM 3.50 per USD in January 2015 to RM 4.47 per USD in December 2015 based on the baseline data. For short-term ex-ante forecast, the analysis results indicate a decrease in exchange rate on 2016 June (RM 4.27 per USD) as compared with 2015 December. A more appropriate forecasting method of exchange rate is vital to aid the decision-making process and planning on the sustainable commodities' production in the world economy.

Mixture EMOS model for calibrating ensemble forecasts of wind speed.

PubMed

Baran, S; Lerch, S

2016-03-01

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.

Value versus Accuracy: application of seasonal forecasts to a hydro-economic optimization model for the Sudanese Blue Nile

NASA Astrophysics Data System (ADS)

Satti, S.; Zaitchik, B. F.; Siddiqui, S.; Badr, H. S.; Shukla, S.; Peters-Lidard, C. D.

2015-12-01

The unpredictable nature of precipitation within the East African (EA) region makes it one of the most vulnerable, food insecure regions in the world. There is a vital need for forecasts to inform decision makers, both local and regional, and to help formulate the region's climate change adaptation strategies. Here, we present a suite of different seasonal forecast models, both statistical and dynamical, for the EA region. Objective regionalization is performed for EA on the basis of interannual variability in precipitation in both observations and models. This regionalization is applied as the basis for calculating a number of standard skill scores to evaluate each model's forecast accuracy. A dynamically linked Land Surface Model (LSM) is then applied to determine forecasted flows, which drive the Sudanese Hydroeconomic Optimization Model (SHOM). SHOM combines hydrologic, agronomic and economic inputs to determine the optimal decisions that maximize economic benefits along the Sudanese Blue Nile. This modeling sequence is designed to derive the potential added value of information of each forecasting model to agriculture and hydropower management. A rank of each model's forecasting skill score along with its added value of information is analyzed in order compare the performance of each forecast. This research aims to improve understanding of how characteristics of accuracy, lead time, and uncertainty of seasonal forecasts influence their utility to water resources decision makers who utilize them.

Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Y.; Rong, X.; Liu, Z.

2017-12-01

This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.

Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Feng, Cong; Cui, Mingjian

Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern ismore » determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.« less

Improving wave forecasting by integrating ensemble modelling and machine learning

NASA Astrophysics Data System (ADS)

O'Donncha, F.; Zhang, Y.; James, S. C.

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

A Unified Data Assimilation Strategy for Regional Coupled Atmosphere-Ocean Prediction Systems

NASA Astrophysics Data System (ADS)

Xie, Lian; Liu, Bin; Zhang, Fuqing; Weng, Yonghui

2014-05-01

Improving tropical cyclone (TC) forecasts is a top priority in weather forecasting. Assimilating various observational data to produce better initial conditions for numerical models using advanced data assimilation techniques has been shown to benefit TC intensity forecasts, whereas assimilating large-scale environmental circulation into regional models by spectral nudging or Scale-Selective Data Assimilation (SSDA) has been demonstrated to improve TC track forecasts. Meanwhile, taking into account various air-sea interaction processes by high-resolution coupled air-sea modelling systems has also been shown to improve TC intensity forecasts. Despite the advances in data assimilation and air-sea coupled models, large errors in TC intensity and track forecasting remain. For example, Hurricane Nate (2011) has brought considerable challenge for the TC operational forecasting community, with very large intensity forecast errors (27, 25, and 40 kts for 48, 72, and 96 h, respectively) for the official forecasts. Considering the slow-moving nature of Hurricane Nate, it is reasonable to hypothesize that air-sea interaction processes played a critical role in the intensity change of the storm, and accurate representation of the upper ocean dynamics and thermodynamics is necessary to quantitatively describe the air-sea interaction processes. Currently, data assimilation techniques are generally only applied to hurricane forecasting in stand-alone atmospheric or oceanic model. In fact, most of the regional hurricane forecasting models only included data assimilation techniques for improving the initial condition of the atmospheric model. In such a situation, the benefit of adjustments in one model (atmospheric or oceanic) by assimilating observational data can be compromised by errors from the other model. Thus, unified data assimilation techniques for coupled air-sea modelling systems, which not only simultaneously assimilate atmospheric and oceanic observations into the coupled air-sea modelling system, but also nudging the large-scale environmental flow in the regional model towards global model forecasts are of increasing necessity. In this presentation, we will outline a strategy for an integrated approach in air-sea coupled data assimilation and discuss its benefits and feasibility from incremental results for select historical hurricane cases.

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less

Two-step forecast of geomagnetic storm using coronal mass ejection and solar wind condition

PubMed Central

Kim, R-S; Moon, Y-J; Gopalswamy, N; Park, Y-D; Kim, Y-H

2014-01-01

To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz ≤ −5 nT or Ey ≥ 3 mV/m for t≥ 2 h for moderate storms with minimum Dst less than −50 nT) and a Dst model developed by Temerin and Li (2002, 2006) (TL model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90%) than the forecasts based on the TL model (87%). However, the latter produces better forecasts for 24 nonstorm events (88%), while the former correctly forecasts only 71% of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80%) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (∩), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81%) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (∪), all geomagnetic storms are correctly forecasted. PMID:26213515

Two-step forecast of geomagnetic storm using coronal mass ejection and solar wind condition.

PubMed

Kim, R-S; Moon, Y-J; Gopalswamy, N; Park, Y-D; Kim, Y-H

2014-04-01

To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study ( B z  ≤ -5 nT or E y  ≥ 3 mV/m for t ≥ 2 h for moderate storms with minimum Dst less than -50 nT) and a Dst model developed by Temerin and Li (2002, 2006) (TL model). Using 55 CME- Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90%) than the forecasts based on the TL model (87%). However, the latter produces better forecasts for 24 nonstorm events (88%), while the former correctly forecasts only 71% of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80%) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (∩), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81%) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (∪), all geomagnetic storms are correctly forecasted.

Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

EPA Science Inventory

High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

Forecasting the Impact of Technological Change on Manpower Utilization and Displacement: An Analytic Summary.

ERIC Educational Resources Information Center

Fechter, Alan

Obstacles to producing forecasts of the impact of technological change and skill utilization are briefly discussed, and existing models for forecasting manpower requirements are described and analyzed. A survey of current literature reveals a concentration of models for producing long-range national forecasts, but few models for generating…

Regional Air Quality forecAST (RAQAST) Over the U.S

NASA Astrophysics Data System (ADS)

Yoshida, Y.; Choi, Y.; Zeng, T.; Wang, Y.

2005-12-01

A regional chemistry and transport modeling system is used to provide 48-hour forecast of the concentrations of ozone and its precursors over the United States. Meteorological forecast is conducted using the NCAR/Penn State MM5 model. The regional chemistry and transport model simulates the sources, transport, chemistry, and deposition of 24 chemical tracers. The lateral and upper boundary conditions of trace gas concentrations are specified using the monthly mean output from the global GEOS-CHEM model. The initial and boundary conditions for meteorological fields are taken from the NOAA AVN forecast. The forecast has been operational since August, 2003. Model simulations are evaluated using surface, aircraft, and satellite measurements in the A'hindcast' mode. The next step is an automated forecast evaluation system.

Demand forecast model based on CRM

NASA Astrophysics Data System (ADS)

Cai, Yuancui; Chen, Lichao

2006-11-01

With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

A New Multivariate Approach in Generating Ensemble Meteorological Forcings for Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Khajehei, Sepideh; Moradkhani, Hamid

2015-04-01

Producing reliable and accurate hydrologic ensemble forecasts are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model structure, and model parameters. Producing reliable and skillful precipitation ensemble forecasts is one approach to reduce the total uncertainty in hydrological applications. Currently, National Weather Prediction (NWP) models are developing ensemble forecasts for various temporal ranges. It is proven that raw products from NWP models are biased in mean and spread. Given the above state, there is a need for methods that are able to generate reliable ensemble forecasts for hydrological applications. One of the common techniques is to apply statistical procedures in order to generate ensemble forecast from NWP-generated single-value forecasts. The procedure is based on the bivariate probability distribution between the observation and single-value precipitation forecast. However, one of the assumptions of the current method is fitting Gaussian distribution to the marginal distributions of observed and modeled climate variable. Here, we have described and evaluated a Bayesian approach based on Copula functions to develop an ensemble precipitation forecast from the conditional distribution of single-value precipitation forecasts. Copula functions are known as the multivariate joint distribution of univariate marginal distributions, which are presented as an alternative procedure in capturing the uncertainties related to meteorological forcing. Copulas are capable of modeling the joint distribution of two variables with any level of correlation and dependency. This study is conducted over a sub-basin in the Columbia River Basin in USA using the monthly precipitation forecasts from Climate Forecast System (CFS) with 0.5x0.5 Deg. spatial resolution to reproduce the observations. The verification is conducted on a different period and the superiority of the procedure is compared with Ensemble Pre-Processor approach currently used by National Weather Service River Forecast Centers in USA.

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions

NASA Astrophysics Data System (ADS)

Pérez, B.; Brouwer, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hackett, B.; Verlaan, M.; Fanjul, E. A.

2012-03-01

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.

Do quantitative decadal forecasts from GCMs provide decision relevant skill?

NASA Astrophysics Data System (ADS)

Suckling, E. B.; Smith, L. A.

2012-04-01

It is widely held that only physics-based simulation models can capture the dynamics required to provide decision-relevant probabilistic climate predictions. This fact in itself provides no evidence that predictions from today's GCMs are fit for purpose. Empirical (data-based) models are employed to make probability forecasts on decadal timescales, where it is argued that these 'physics free' forecasts provide a quantitative 'zero skill' target for the evaluation of forecasts based on more complicated models. It is demonstrated that these zero skill models are competitive with GCMs on decadal scales for probability forecasts evaluated over the last 50 years. Complications of statistical interpretation due to the 'hindcast' nature of this experiment, and the likely relevance of arguments that the lack of hindcast skill is irrelevant as the signal will soon 'come out of the noise' are discussed. A lack of decision relevant quantiative skill does not bring the science-based insights of anthropogenic warming into doubt, but it does call for a clear quantification of limits, as a function of lead time, for spatial and temporal scales on which decisions based on such model output are expected to prove maladaptive. Failing to do so may risk the credibility of science in support of policy in the long term. The performance amongst a collection of simulation models is evaluated, having transformed ensembles of point forecasts into probability distributions through the kernel dressing procedure [1], according to a selection of proper skill scores [2] and contrasted with purely data-based empirical models. Data-based models are unlikely to yield realistic forecasts for future climate change if the Earth system moves away from the conditions observed in the past, upon which the models are constructed; in this sense the empirical model defines zero skill. When should a decision relevant simulation model be expected to significantly outperform such empirical models? Probability forecasts up to ten years ahead (decadal forecasts) are considered, both on global and regional spatial scales for surface air temperature. Such decadal forecasts are not only important in terms of providing information on the impacts of near-term climate change, but also from the perspective of climate model validation, as hindcast experiments and a sufficient database of historical observations allow standard forecast verification methods to be used. Simulation models from the ENSEMBLES hindcast experiment [3] are evaluated and contrasted with static forecasts of the observed climatology, persistence forecasts and against simple statistical models, called dynamic climatology (DC). It is argued that DC is a more apropriate benchmark in the case of a non-stationary climate. It is found that the ENSEMBLES models do not demonstrate a significant increase in skill relative to the empirical models even at global scales over any lead time up to a decade ahead. It is suggested that the contsruction and co-evaluation with the data-based models become a regular component of the reporting of large simulation model forecasts. The methodology presented may easily be adapted to other forecasting experiments and is expected to influence the design of future experiments. The inclusion of comparisons with dynamic climatology and other data-based approaches provide important information to both scientists and decision makers on which aspects of state-of-the-art simulation forecasts are likely to be fit for purpose. [1] J. Bröcker and L. A. Smith. From ensemble forecasts to predictive distributions, Tellus A, 60(4), 663-678 (2007). [2] J. Bröcker and L. A. Smith. Scoring probabilistic forecasts: The importance of being proper, Weather and Forecasting, 22, 382-388 (2006). [3] F. J. Doblas-Reyes, A. Weisheimer, T. N. Palmer, J. M. Murphy and D. Smith. Forecast quality asessment of the ENSEMBLES seasonal-to-decadal stream 2 hindcasts, ECMWF Technical Memorandum, 621 (2010).

An empirical investigation on different methods of economic growth rate forecast and its behavior from fifteen countries across five continents

NASA Astrophysics Data System (ADS)

Yin, Yip Chee; Hock-Eam, Lim

2012-09-01

Our empirical results show that we can predict GDP growth rate more accurately in continent with fewer large economies, compared to smaller economies like Malaysia. This difficulty is very likely positively correlated with subsidy or social security policies. The stage of economic development and level of competiveness also appears to have interactive effects on this forecast stability. These results are generally independent of the forecasting procedures. Countries with high stability in their economic growth, forecasting by model selection is better than model averaging. Overall forecast weight averaging (FWA) is a better forecasting procedure in most countries. FWA also outperforms simple model averaging (SMA) and has the same forecasting ability as Bayesian model averaging (BMA) in almost all countries.

Model Forecast Skill and Sensitivity to Initial Conditions in the Seasonal Sea Ice Outlook

NASA Technical Reports Server (NTRS)

Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

2015-01-01

We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

PubMed

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

Pulling Econometrics Students up by Their Bootstraps

ERIC Educational Resources Information Center

O'Hara, Michael E.

2014-01-01

Although the concept of the sampling distribution is at the core of much of what we do in econometrics, it is a concept that is often difficult for students to grasp. The thought process behind bootstrapping provides a way for students to conceptualize the sampling distribution in a way that is intuitive and visual. However, teaching students to…

The Nexus of Place and Finance in the Analysis of Educational Attainment: A Spatial Econometric Approach

ERIC Educational Resources Information Center

Sutton, Farah

2012-01-01

This study examines the spatial distribution of educational attainment and then builds upon current predictive frameworks for understanding patterns of educational attainment by applying a spatial econometric method of analysis. The research from this study enables a new approach to the policy discussion on how to improve educational attainment…

First-Year Study Success in Economics and Econometrics: The Role of Gender, Motivation, and Math Skills

ERIC Educational Resources Information Center

Arnold, Ivo J. M.; Rowaan, Wietske

2014-01-01

In this study, the authors investigate the relationships among gender, math skills, motivation, and study success in economics and econometrics. They find that female students have stronger intrinsic motivation, yet lower study confidence than their male counterparts. They also find weak evidence for a gender gap over the entire first-year…

Three-model ensemble wind prediction in southern Italy

NASA Astrophysics Data System (ADS)

Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

2016-03-01

Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

Statistical and dynamical forecast of regional precipitation after mature phase of ENSO

NASA Astrophysics Data System (ADS)

Sohn, S.; Min, Y.; Lee, J.; Tam, C.; Ahn, J.

2010-12-01

While the seasonal predictability of general circulation models (GCMs) has been improved, the current model atmosphere in the mid-latitude does not respond correctly to external forcing such as tropical sea surface temperature (SST), particularly over the East Asia and western North Pacific summer monsoon regions. In addition, the time-scale of prediction scope is considerably limited and the model forecast skill still is very poor beyond two weeks. Although recent studies indicate that coupled model based multi-model ensemble (MME) forecasts show the better performance, the long-lead forecasts exceeding 9 months still show a dramatic decrease of the seasonal predictability. This study aims at diagnosing the dynamical MME forecasts comprised of the state of art 1-tier models as well as comparing them with the statistical model forecasts, focusing on the East Asian summer precipitation predictions after mature phase of ENSO. The lagged impact of El Nino as major climate contributor on the summer monsoon in model environments is also evaluated, in the sense of the conditional probabilities. To evaluate the probability forecast skills, the reliability (attributes) diagram and the relative operating characteristics following the recommendations of the World Meteorological Organization (WMO) Standardized Verification System for Long-Range Forecasts are used in this study. The results should shed light on the prediction skill for dynamical model and also for the statistical model, in forecasting the East Asian summer monsoon rainfall with a long-lead time.

National Centers for Environmental Prediction

Science.gov Websites

Products Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model PARALLEL/EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

National Centers for Environmental Prediction

Science.gov Websites

Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model Configuration /EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION / DIAGNOSTICS Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.

Forecasting the mortality rates of Indonesian population by using neural network

NASA Astrophysics Data System (ADS)

Safitri, Lutfiani; Mardiyati, Sri; Rahim, Hendrisman

2018-03-01

A model that can represent a problem is required in conducting a forecasting. One of the models that has been acknowledged by the actuary community in forecasting mortality rate is the Lee-Certer model. Lee Carter model supported by Neural Network will be used to calculate mortality forecasting in Indonesia. The type of Neural Network used is feedforward neural network aligned with backpropagation algorithm in python programming language. And the final result of this study is mortality rate in forecasting Indonesia for the next few years

Potential Technologies for Assessing Risk Associated with a Mesoscale Forecast

DTIC Science & Technology

2015-10-01

American GFS models, and informally applied on the Weather Research and Forecasting ( WRF ) model. The current CI equation is as follows...Reen B, Penc R. Investigating surface bias errors in the Weather Research and Forecasting ( WRF ) model using a Geographic Information System (GIS). J...Forecast model ( WRF -ARW) with extensions that might include finer terrain resolutions and more detailed representations of the underlying atmospheric

Evaluation of regression and neural network models for solar forecasting over different short-term horizons

DOE PAGES

Inanlouganji, Alireza; Reddy, T. Agami; Katipamula, Srinivas

2018-04-13

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar photovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for different forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with different climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term fluctuations, different variants of themore » lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an artificial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict different short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Lastly, the effect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloud cover data.« less

Skill of Ensemble Seasonal Probability Forecasts

NASA Astrophysics Data System (ADS)

Smith, Leonard A.; Binter, Roman; Du, Hailiang; Niehoerster, Falk

2010-05-01

In operational forecasting, the computational complexity of large simulation models is, ideally, justified by enhanced performance over simpler models. We will consider probability forecasts and contrast the skill of ENSEMBLES-based seasonal probability forecasts of interest to the finance sector (specifically temperature forecasts for Nino 3.4 and the Atlantic Main Development Region (MDR)). The ENSEMBLES model simulations will be contrasted against forecasts from statistical models based on the observations (climatological distributions) and empirical dynamics based on the observations but conditioned on the current state (dynamical climatology). For some start dates, individual ENSEMBLES models yield significant skill even at a lead-time of 14 months. The nature of this skill is discussed, and chances of application are noted. Questions surrounding the interpretation of probability forecasts based on these multi-model ensemble simulations are then considered; the distributions considered are formed by kernel dressing the ensemble and blending with the climatology. The sources of apparent (RMS) skill in distributions based on multi-model simulations is discussed, and it is demonstrated that the inclusion of "zero-skill" models in the long range can improve Root-Mean-Square-Error scores, casting some doubt on the common justification for the claim that all models should be included in forming an operational probability forecast. It is argued that the rational response varies with lead time.

Evaluation of regression and neural network models for solar forecasting over different short-term horizons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Inanlouganji, Alireza; Reddy, T. Agami; Katipamula, Srinivas

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar photovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for different forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with different climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term fluctuations, different variants of themore » lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an artificial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict different short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Lastly, the effect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloud cover data.« less

Skill of a global seasonal ensemble streamflow forecasting system

NASA Astrophysics Data System (ADS)

Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc

2013-04-01

Forecasting of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable seasonal streamflow forecasts are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be valuable especially for developing regions of the world, where effective hydrological forecasting systems are scarce. In this study, we investigate the forecasting skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCR-GLOBWB. FEWS-World has been setup within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The assessment in historical simulation mode used a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF). We assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes in 20 large rivers of the world. This preliminary assessment concluded that the prospects for seasonal forecasting with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological forecasts. Thus the meteorological forcing errors were not assessed. Yet, in a forecasting setup, the predictive skill of a hydrological forecasting system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive forecasting mode, the model is forced with actual ensemble forecasts from the seasonal forecast archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of the year and for lead times up to 6 months. The forecasted discharges are compared with observed monthly streamflow records using the ensemble verification measures Brier Skill Score (BSS) and Continuous Ranked Probability Score (CRPS). The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world.

Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

NASA Astrophysics Data System (ADS)

Subramanian, Aneesh C.; Palmer, Tim N.

2017-06-01

Stochastic schemes to represent model uncertainty in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium-range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October-November 2011. These are well-studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large-scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.