WorldWideScience

Sample records for models econometrically forecast

  1. Econometric Models for Forecasting of Macroeconomic Indices

    Science.gov (United States)

    Sukhanova, Elena I.; Shirnaeva, Svetlana Y.; Mokronosov, Aleksandr G.

    2016-01-01

    The urgency of the research topic was stipulated by the necessity to carry out an effective controlled process by the economic system which can hardly be imagined without indices forecasting characteristic of this system. An econometric model is a safe tool of forecasting which makes it possible to take into consideration the trend of indices…

  2. An Econometric Model for Forecasting Income and Employment in Hawaii.

    Science.gov (United States)

    Chau, Laurence C.

    This report presents the methodology for short-run forecasting of personal income and employment in Hawaii. The econometric model developed in the study is used to make actual forecasts through 1973 of income and employment, with major components forecasted separately. Several sets of forecasts are made, under different assumptions on external…

  3. ECONOMIC FORECASTS BASED ON ECONOMETRIC MODELS USING EViews 5

    Directory of Open Access Journals (Sweden)

    Cornelia TomescuDumitrescu,

    2009-05-01

    Full Text Available The forecast of evolution of economic phenomena represent on the most the final objective of econometrics. It withal represent a real attempt of validity elaborate model. Unlike the forecasts based on the study of temporal series which have an recognizable inertial character the forecasts generated by econometric model with simultaneous equations are after to contour the future of ones of important economic variables toward the direct and indirect influences bring the bear on their about exogenous variables. For the relief of the calculus who the realization of the forecasts based on the econometric models its suppose is indicate the use of the specialized informatics programs. One of this is the EViews which is applied because it reduces significant the time who is destined of the econometric analysis and it assure a high accuracy of calculus and of the interpretation of results.

  4. A national econometric forecasting model of the dental sector.

    OpenAIRE

    Feldstein, P J; Roehrig, C S

    1980-01-01

    The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, th...

  5. Econometrics 101: forecasting demystified

    Energy Technology Data Exchange (ETDEWEB)

    Crow, R.T.

    1980-05-01

    Forecasting by econometric modeling is described in a commonsense way which omits much of the technical jargon. A trend of continuous growth is no longer an adequate forecasting tool. Today's forecasters must consider rapid changes in price, policies, regulations, capital availability, and the cost of being wrong. A forecasting model is designed by identifying future influences on electricity purchases and quantifying their relationships to each other. A record is produced which can be evaluated and used to make corrections in the models. Residential consumption is used to illustrate how this works and to demonstrate how power consumption is also related to the purchase and use of equipment. While models can quantify behavioral relationships, they cannot account for the impacts of non-price factors because of limited data. (DCK)

  6. Econometric Forecasting Models for Air Traffic Passenger of Indonesia

    Directory of Open Access Journals (Sweden)

    Viktor Suryan

    2017-01-01

    Full Text Available One of the major benefits of the air transport services operating in bigger countries is the fact that they provide a vital social economic linkage. This study is an attempt to establish the determinants of the passenger air traffic in Indonesia. The main objective of the study is to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of panel data and to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of time series data. This research also predicts the upcoming number of air traffic passenger until 2030. Air transportation and the economic activity in a country are interdependent. This work first uses the data at the country level and then at the selected airport level for review. The methodology used in this study has adopted the study for both normal regression and panel data regression techniques. Once all these steps are performed, the final equation is taken up for the forecast of the passenger inflow data in the Indonesian airports. To forecast the same, the forecasted numbers of the GDP (Gross Domestic Product and population (independent variables were chosen as a part of the literature review exercise are used. The result of this study shows the GDP per capita have significant related to a number of passengers which the elasticity 2.23 (time-series data and 1.889 for panel data. The exchange rate variable is unrelated to a number of passengers as shown in the value of elasticity. In addition, the total of population gives small value for the elasticity. Moreover, the number of passengers is also affected by the dummy variable (deregulation. With three scenarios: low, medium and high for GDP per capita, the percentage of growth for total number of air traffic passenger from the year 2015 to 2030 is 199.3%, 205.7%, and 320.9% respectively.

  7. BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

    Directory of Open Access Journals (Sweden)

    Mihaela Simionescu

    2014-12-01

    Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

  8. Econometric modelling

    Directory of Open Access Journals (Sweden)

    M. Alguacil Marí

    2017-08-01

    Full Text Available The current economic environment, together with the low scores obtained by our students in recent years, makes it necessary to incorporate new teaching methods. In this sense, econometric modelling provides a unique opportunity offering to the student with the basic tools to address the study of Econometrics in a deeper and novel way. In this article, this teaching method is described, presenting also an example based on a recent study carried out by two students of the Degree of Economics. Likewise, the success of this method is evaluated quantitatively in terms of academic performance. The results confirm our initial idea that the greater involvement of the student, as well as the need for a more complete knowledge of the subject, suppose a stimulus for the study of this subject. As evidence of this, we show how those students who opted for the method we propose here obtained higher qualifications than those that chose the traditional method.

  9. Forecasting the EMU inflation rate: Linear econometric vs. non-linear computational models using genetic neural fuzzy systems

    DEFF Research Database (Denmark)

    Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric

    2004-01-01

    This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according...... to a battery of parametric and non-parametric test statistics to measure their performance in one- and four-step ahead forecasts of quarterly data. Using genetic-neural fuzzy systems we find the computational approach superior to some degree and show how to combine both techniques successfully....

  10. Evaluating Econometric Models and Expert Intuition

    NARCIS (Netherlands)

    R. Legerstee (Rianne)

    2012-01-01

    textabstractThis thesis is about forecasting situations which involve econometric models and expert intuition. The first three chapters are about what it is that experts do when they adjust statistical model forecasts and what might improve that adjustment behavior. It is investigated how expert for

  11. Econometric models for biohydrogen development.

    Science.gov (United States)

    Lee, Duu-Hwa; Lee, Duu-Jong; Veziroglu, Ayfer

    2011-09-01

    Biohydrogen is considered as an attractive clean energy source due to its high energy content and environmental-friendly conversion. Analyzing various economic scenarios can help decision makers to optimize development strategies for the biohydrogen sector. This study surveys econometric models of biohydrogen development, including input-out models, life-cycle assessment approach, computable general equilibrium models, linear programming models and impact pathway approach. Fundamentals of each model were briefly reviewed to highlight their advantages and disadvantages. The input-output model and the simplified economic input-output life-cycle assessment model proved most suitable for economic analysis of biohydrogen energy development. A sample analysis using input-output model for forecasting biohydrogen development in the United States is given.

  12. ECONOMETRIC FORECAST OF AGRICULTURAL SECTOR INVESTING IN LVOV REGION

    Directory of Open Access Journals (Sweden)

    Rostyslav Lytvyn

    2014-07-01

    Full Text Available Purpose of economic processes forecasting in agriculture is more relevant and urgent in recent years with application of applied econometric methods. In represented research paper, these methods are used to forecast investment and the main agricultural industry indicators of Lvov region of Ukraine. The linear trend model, the parabolic trend model and the exponential trend model were elaborated from the period from 2000 to 2009 in this scientific study using applied statistical tool STATGRAFICS and EXCEL spreadsheets. And with assistance of these models forecast for investment on the basis of data of essential indicators of agrarian sector of the region for 2010 and 2011 was made. All models with probability р=0,95 are adequate experimental data for 2000-2009 years, that allow to make the forecast of investments and main agricultural indicators of the researched region by these models for 2010 and 2011 years. Nevertheless, it should be pointed out that, because of small amount of input data analysis of regression equations coefficients have more qualitative than quantitative influence upon resulting variable y6.

  13. An investigation of forecast horizon and observation fit’s influence on an econometric rate forecast model in the liner shipping industry

    DEFF Research Database (Denmark)

    Nielsen, Peter; Jiang, Liping; Rytter, Niels Gorm Malý

    2014-01-01

    This paper evaluates the influence of forecast horizon and observation fit on the robustness and performance of a specific freight rate forecast model used in the liner shipping industry. In the first stage of the research, a forecast model used to predict container freight rate development...... of the forecast horizon and observation fit and their interactions on the forecast model's performance. The results underline the complicated nature of creating a suitable forecast model by balancing business needs, a desire to fit a good model and achieve high accuracy. There is strong empirical evidence from...... this study; that a robust model is preferable, that overfitting is a true danger, and that a balance must be achieved between forecast horizon and the number of observations used to fit the model. In addition, methodological guidance has also been provided on how to test, design, and choose the superior...

  14. ECONOMETRIC MODELS FOR DETERMING THE EXCHANGE RATE

    Directory of Open Access Journals (Sweden)

    Mihaela BRATU

    2012-05-01

    Full Text Available The simple econometric models for the exchange rate, according to recent researches, generates the forecasts with the highest degree of accuracy. This type of models (Simultaneous Equations Model, MA(1 Procedure, Model with lagged variables is used to describe the evolution of the average exchange rate in Romanian in January 1991-March 2012 and to predict it on short run. The best forecasts, in terms of accuracy, on the forecasting horizon April-May 2012 were those based on a Simultaneous Equations Model that takes into account the Granger causality. An almost high degree of accuracy was gotten by combining the predictions based on MA(1 model with those based on the simultaneous equations model, when INV weighting scheme was applied (the forecasts are inversely weighted to their relative mean squared forecast error. The lagged variables Model provided the highest prediction errors. The importance of knowing the best exchange rate forecasts is related to the improvement of decision-making and the building of the monetary policy.

  15. Crystal study and econometric model

    Science.gov (United States)

    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.

  16. Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator

    Science.gov (United States)

    Fernández-Vázquez, Esteban; Moreno, Blanca

    2017-08-01

    Forecast combination has been studied in econometrics for a long time, and the literature has shown the superior performance of forecast combination over individual predictions. However, there is still controversy on which is the best procedure to specify the forecast weights. This paper explores the possibility of using a procedure based on Entropy Econometrics, which allows setting the weights for the individual forecasts as a mixture of different alternatives. In particular, we examine the ability of the Data-Weighted Prior Estimator proposed by Golan (J Econom 101(1):165-193, 2001) to combine forecasting models in a context of small sample sizes, a relative common scenario when dealing with time series for regional economies. We test the validity of the proposed approach using a simulation exercise and a real-world example that aims at predicting gross regional product growth rates for a regional economy. The forecasting performance of the Data-Weighted Prior Estimator proposed is compared with other combining methods. The simulation results indicate that in scenarios of heavily ill-conditioned datasets the approach suggested dominates other forecast combination strategies. The empirical results are consistent with the conclusions found in the numerical experiment.

  17. Econometric Model – A Tool in Financial Management

    Directory of Open Access Journals (Sweden)

    Riana Iren RADU

    2011-06-01

    Full Text Available The economic situation in Romania requires from the trader a rigorous analysis of vulnerabilities and opportunities offered by the external environment and a careful analysis of internal environmental conditions in which the entity operates. In this context particular attention is paid to indicators presented in the financial statements. Many times they are a model for economic forecasts, future plans, basic business and businesses that use them with a good forecasting activity. In this paper we propose to analyze the comparative evolution of the main financial indicators highlighted in financial statements (profit and loss through a multi-equation econometric model, namely dynamic Keynesian model.

  18. Econometrics

    OpenAIRE

    Creel, Michael

    2003-01-01

    This is a project to develop a document for teaching graduate econometrics that is "open source", specifically, licensed as GNU GPL. That is, anyone can access the document in editable form, and can modify it, as long as they make their modifications available. This allows for personalization, as well as a simple way to make contributions and error corrections. The hope is that people preparing to teach econometrics for the first time might find it useful, and eventually be motivated to contr...

  19. Econometrics

    OpenAIRE

    Creel, Michael

    2006-01-01

    This is a project to develop a document for teaching graduate econometrics that is "open source", specifically, licensed as GNU GPL. That is, anyone can access the document in editable form, and can modify it, as long as they make their modifications available. This allows for personalization, as well as a simple way to make contributions and error corrections. The hope is that people preparing to teach econometrics for the first time might find it useful, and eventually be motivated to contr...

  20. Econometric Model Evaluation: Implications for Program Evaluation.

    Science.gov (United States)

    Ridge, Richard S.; And Others

    1990-01-01

    The problem associated with evaluating an econometric model using values outside those used in the model estimation is illustrated in the evaluations of a residential load management program during each of two successive years. Analysis reveals that attention must be paid to this problem. (Author/TJH)

  1. On the econometrics of the Koyck model

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R.D. van Oest (Rutger)

    2004-01-01

    textabstractThe geometric distributed lag model, after application of the so-called Koyck transformation, is often used to establish the dynamic link between sales and advertising. This year, the Koyck model celebrates its 50th anniversary.In this paper we focus on the econometrics of this popular

  2. Spatial Econometric data analysis: moving beyond traditional models

    NARCIS (Netherlands)

    Florax, R.J.G.M.; Vlist, van der A.J.

    2003-01-01

    This article appraises recent advances in the spatial econometric literature. It serves as the introduction too collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling perspecti

  3. Energy: modelization and econometrics. Proceedings of colloquium

    Energy Technology Data Exchange (ETDEWEB)

    Fericelli, J.; Lesourd, J.B.

    1985-01-01

    The document presents the communications of the ''applied econometric association'' symposium and introduces the description of various French and foreigner models: analysis of the energy demand and production functions with energy input. A detailed evaluation of the Translog function applied to energy is described. Other energy economic aspects are approched: energy prices and costs, energetic balances, energy management in enterprises, impact evaluation of alternative energy policies.

  4. Econometric models for predicting confusion crop ratios

    Science.gov (United States)

    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.

  5. Kriging Methodology and Its Development in Forecasting Econometric Time Series

    Directory of Open Access Journals (Sweden)

    Andrej Gajdoš

    2017-03-01

    Full Text Available One of the approaches for forecasting future values of a time series or unknown spatial data is kriging. The main objective of the paper is to introduce a general scheme of kriging in forecasting econometric time series using a family of linear regression time series models (shortly named as FDSLRM which apply regression not only to a trend but also to a random component of the observed time series. Simultaneously performing a Monte Carlo simulation study with a real electricity consumption dataset in the R computational langure and environment, we investigate the well-known problem of “negative” estimates of variance components when kriging predictions fail. Our following theoretical analysis, including also the modern apparatus of advanced multivariate statistics, gives us the formulation and proof of a general theorem about the explicit form of moments (up to sixth order for a Gaussian time series observation. This result provides a basis for further theoretical and computational research in the kriging methodology development.

  6. Econometrics

    OpenAIRE

    ROMBOUTS, Jeroen V.K.; Bauwens, Luc

    2004-01-01

    Since the last decade we live in a digitalized world where many actions in human and economic life are monitored. This produces a continuous stream of new, rich and high quality data in the form of panels, repeated cross-sections and long time series . These data resources are available to many researchers at a low cost. This new erais fascinating for econometricians who can adress many open economic questions. To do so, new models are developed that call for elaborate estimation techniques. ...

  7. ECONOMETRIC APPROACH TO DIFFERENCE EQUATIONS MODELING OF EXCHANGE RATES CHANGES

    Directory of Open Access Journals (Sweden)

    Josip Arnerić

    2010-12-01

    Full Text Available Time series models that are commonly used in econometric modeling are autoregressive stochastic linear models (AR and models of moving averages (MA. Mentioned models by their structure are actually stochastic difference equations. Therefore, the objective of this paper is to estimate difference equations containing stochastic (random component. Estimated models of time series will be used to forecast observed data in the future. Namely, solutions of difference equations are closely related to conditions of stationary time series models. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modeling time varying volatility are GARCH type models and their variants. However, GARCH models will not be analyzed because the purpose of this research is to predict the value of the exchange rate in the levels within conditional mean equation and to determine whether the observed variable has a stable or explosive time path. Based on the estimated difference equation it will be examined whether Croatia is implementing a stable policy of exchange rates.

  8. Micro Econometric Modelling of Household Energy Use

    DEFF Research Database (Denmark)

    Leth-Petersen, Søren

    2002-01-01

    Presents a micro econometric analysis of household electricity and natural gas demand for Danish households observed in 1996. Dependence between demand for gas and demand for electricity; Separability of demand for gas from demand for electricity; Relation between energy consumption and the age...

  9. Micro Econometric Modelling of Household Energy Use

    DEFF Research Database (Denmark)

    Leth-Petersen, Søren

    2002-01-01

    Presents a micro econometric analysis of household electricity and natural gas demand for Danish households observed in 1996. Dependence between demand for gas and demand for electricity; Separability of demand for gas from demand for electricity; Relation between energy consumption and the age...

  10. THE ECONOMETRICS OF THE FORECASTING OF FINANCIAL RESOURCES, A MAIN COMPONENT OF THE FINANCIAL MANAGEMENT

    Directory of Open Access Journals (Sweden)

    2009-05-01

    Full Text Available The paper intends to emphasise the importance of budget resources forecasting for long periods of time, within thefinancial management. An as accurate as possible forecasting of the volume of financial resources will represent the basis forthe future projections of the expenditure of local communities, as they are regulated by law, knowing that one of the principlesrepresenting the basis of budget making is that of the balanced budget. To the same extent, the volume of the budget liquiditieswill depend on the rigorousness of the design of the volume of financial resources.. Beyond the abstract character of themathematic calculus made by specialists in econometrics, the financial manager is also interested to know the forecastingtechniques so that he/she can draw up the income and expenditure budget, the basis for the implementation of the economicsocialdevelopment strategies of the local communities. The financial management remains a fundamental component of thepublic management through the theoretical-methodological arsenal made available for the loan officer.

  11. AN ORIGINAL ECONOMETRIC MODEL OF FDI IN ROMANIA

    Directory of Open Access Journals (Sweden)

    Gheorghe SĂVOIU

    2012-04-01

    Full Text Available The central theme of this paper is, as the title itself shows, the econometric modelling of Foreign Direct Investments (FDI, based on the concept Euromoney’ s country risk rating. This article contains three sections, the first part or the introduction is an approach of investment risk and, in particular, introduces a new element in modelling investment, namely country risk rating. Thus, a bridge is created towards the second section, which essentially deals with the econometric modelling of foreign direct investment (FDI in Romania, after 1996, based on Euromoney’s data (ECR. The originality of this paper is underlined by the presence of a final model which includes, as an exogenous variable, country risk rating in assessing the FDI share of GDP as an endogenous variable. A final remark comments, from an economic perspective, the results of the econometric modelling.

  12. Investigation of international energy economics. [Use of econometric model EXPLOR

    Energy Technology Data Exchange (ETDEWEB)

    Deonigi, D.E.; Clement, M.; Foley, T.J.; Rao, S.A.

    1977-03-01

    The Division of International Affairs of the Energy Research and Development Administration is assessing the long-range economic effects of energy research and development programs in the U.S. and other countries, particularly members of the International Energy Agency (IEA). In support of this effort, a program was designed to coordinate the capabilities of five research groups--Rand, Virginia Polytechnic Institute, Brookhaven National Laboratory, Lawrence Livermore Laboratory, and Pacific Northwest Laboratory. The program could evaluate the international economics of proposed or anticipated sources of energy. This program is designed to be general, flexible, and capable of evaluating a diverse collection of potential energy (nuclear and nonnuclear) related problems. For example, the newly developed methodology could evaluate the international and domestic economic impact of nuclear-related energy sources, but also existing nonnuclear and potential energy sources such as solar, geothermal, wind, etc. Major items to be included would be the cost of exploration, cost of production, prices, profit, market penetration, investment requirements and investment goods, economic growth, change in balance of payments, etc. In addition, the changes in cost of producing all goods and services would be identified for each new energy source. PNL developed (1) a means of estimating the demands for major forms of energy by country, and (2) a means of identifying results or impacts on each country. The results for each country were then to be compared to assess relative advantages. PNL relied on its existing general econometric model, EXPLOR, to forecast the demand for energy by country. (MCW)

  13. Deriving dynamic marketing effectiveness from econometric time series models

    NARCIS (Netherlands)

    C. Horváth (Csilla); Ph.H.B.F. Franses (Philip Hans)

    2003-01-01

    textabstractTo understand the relevance of marketing efforts, it has become standard practice to estimate the long-run and short-run effects of the marketing-mix, using, say, weekly scanner data. A common vehicle for this purpose is an econometric time series model. Issues that are addressed in the

  14. An econometric model of the U.S. pallet market

    Science.gov (United States)

    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...

  15. An Econometric Model for Water Sector in Jordan

    Directory of Open Access Journals (Sweden)

    Mohammed I.  Shahateet

    2008-01-01

    Full Text Available Problem statement: This study argued that there is an economic approach to reduce water problems faced by Jordan. The study took into consideration the increasing population size, the declining rainfall, a deepening shortage of supply and increasing demand for water, the production of agricultural and industrial sectors, price of unit exports, and lack of financial resources. Approach: The framework for a tool which takes into consideration the links between economic growth and the availability of water was developed in this study, in the form of a dynamic simulation econometric model. The model served as a quantitative tool to evaluate the water policy measures and forecast the effect of future policy variables on the water status in Jordan. Results: Agricultural, industrial and other types of production are affected by water uses which in turn are influenced by production and other socioeconomic variables, including population size, the extent of production market, and the size of linkage effects working through certain increases in water consumption. The results also showed the model can be used to solve key issues related to the formulation and implementation of water policy. They also identified lessons for water management policy within a broad socio-economic perspective. Conclusions: First, with regard to production sector, a major effect can be attributed to the supply of water. Second, gross domestic products of agricultural, industrial and other sectors were found to be highly significant factors in influencing the supply of water. Finally, priorities for making the most of Jordan's water resources should be given to options affecting water-supply strategy which relates the supply of water to the level of production.

  16. Forecasting the Polish zloty with non-linear models

    OpenAIRE

    Michal Rubaszek; Pawel Skrzypczynski; Grzegorz Koloch

    2011-01-01

    The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on ex...

  17. 39 CFR 3050.26 - Documentation of demand elasticities and volume forecasts.

    Science.gov (United States)

    2010-07-01

    ... Postal Service shall provide econometric estimates of demand elasticity for all postal products accompanied by the underlying econometric models and the input data sets used; and a volume forecast for the current fiscal year, and the underlying volume forecasting model....

  18. Forecasting telecommunications data with linear models

    OpenAIRE

    Madden, Gary G; Tan, Joachim

    2007-01-01

    For telecommunication companies to successfully manage their business, companies rely on mapping future trends and usage patterns. However, the evolution of telecommunications technology and systems in the provision of services renders imperfections in telecommunications data and impinges on a company’s’ ability to properly evaluate and plan their business. ITU Recommendation E.507 provides a selection of econometric models for forecasting these trends. However, no specific guidance is given....

  19. 基于计量经济学模型的内地赴澳门游客量预测%Forecast on Inbound Visitors Flow from Mainland Chinese to Macau: Based on Econometric Model

    Institute of Scientific and Technical Information of China (English)

    曾忠禄; 郑勇

    2009-01-01

    本研究选择中国大陆地区人均可支配收入、消费者物价指数、国内生产总值,以及澳门与大陆之间商品进出口总额、港币与人民币汇率等变量,建立计量经济学模型估计内地赴澳门游客的数量.模型数据来源于澳门、大陆和香港官方公布的历年季度数据.在建立公式化的预测模型之后,本文还对模型的拟合优度和预测精度给予评估,对模型的基本假设进行了检验.本研究成果可作为有关政府部门和企业的决策参考.%This research built up an econometric model to estimate the possible number of visitors from mainland China to Macau by using the quarterly published official figures including average per person income, consumer price index of Mainland China, the total import and export of both Mainland and Macan Special District, currency exchange rate between Hong Kong dollar and RMB, and gross domestic product of mainland China as the variables. With the prediction model, the paper estimated the goodness of fit and the prediction accuracy of it and tested its basic assumptions. It is expected that the finding of the research could be used as reference for the related government departments and enterprises.

  20. Models and relations in economics and econometrics

    DEFF Research Database (Denmark)

    Juselius, Katarina

    1999-01-01

    Based on a money market analysis using the cointegrated VAR model the paper demonstrates some possible pitfalls in macroeconomic inference as a direct consequence of inadequate stochastic model formulation. A number of questions related to concepts such as empirical and theoretical steady-states,...

  1. Podemos prever a taxa de cambio brasileira? Evidência empírica utilizando inteligência computacional e modelos econométricos Can we forecast Brazilian exchange rates? Empirical evidences using computational intelligence and econometric models

    Directory of Open Access Journals (Sweden)

    Leandro dos Santos Coelho

    2008-12-01

    evidence that these computational intelligence models are able to provide a more accurate forecast given their capacity for capturing nonlinearities and other stylized facts of financial time series. Thus, this paper investigates the hypothesis that the mathematical models of multilayer perception, radial basis function neural networks (NN, and the Takagi-Sugeno (TS fuzzy systems are able to provide a more accurate out-of-sample forecast than the traditional AutoRegressive Moving Average (ARMA and ARMA Generalized AutoRegressive Conditional Heteroskedasticity (ARMA-GARCH models. Using a series of Brazilian exchange rate (R$/US$ returns with 15 minutes, 60 minutes, 120 minutes, daily and weekly basis, the one-step-ahead forecast performance is compared. The results indicate that forecast performance is strongly related to the series' frequency, possibly due to nonlinearities effects. Besides, the forecasting evaluation shows that NN models perform better than the ARMA and ARMA-GARCH ones. In the trade strategy based on forecasts, NN models achieved higher returns when compared to a buy-and-hold strategy and to the other models considered in this study.

  2. The Econometrics Of The Bass Diffusion Model

    NARCIS (Netherlands)

    H.P. Boswijk (Peter); Ph.H.B.F. Franses (Philip Hans)

    2002-01-01

    textabstractWe propose a new empirical representation of the Bass diffusion model, in order to estimate the three key parameters, concerning innovation, imitation and maturity. The representation is based on the notion that the observed data may temporarily deviate from the mean path determined by

  3. MULTIFACTOR ECONOMETRIC MODELS FOR ENERGY EFFICIENCY IN THE EU

    Directory of Open Access Journals (Sweden)

    Gheorghe ZAMAN

    2007-06-01

    Full Text Available The present paper is approaching the energy efficiency topic from the viewpoint of its trends and influence factors, in the context of requirements, criteria and principles of sustainable development. Energy efficiency is measured as ratio of GDP and energy use and its multiple factors of influence are considered. With a view to deducing some conclusions of theoretical-methodological but also of practical-applicative character, we are researching the variation in energy efficiency in European Union, but also in the case of new candidates and other countries, by means of multifactor econometric modeling.

  4. Development Of An Econometric Model Case Study: Romanian Classification System

    Directory of Open Access Journals (Sweden)

    Savescu Roxana

    2015-08-01

    Full Text Available The purpose of the paper is to illustrate an econometric model used to predict the lean meat content in pig carcasses, based on the muscle thickness and back fat thickness measured by the means of an optical probe (OptiGrade PRO.The analysis goes through all steps involved in the development of the model: statement of theory, specification of the mathematical model, sampling and collection of data, estimation of the parameters of the chosen econometric model, tests of the hypothesis derived from the model and prediction equations. The data have been in a controlled experiment conducted by the Romanian Carcass Classification Commission in 2007. The purpose of the experiment was to develop the prediction formulae to be used in the implementation of SEUROP classification system, imposed by European Union legislation. The research methodology used by the author in this study consisted in reviewing the existing literature and normative acts, analyzing the primary data provided by and organization conducting the experiment and interviewing the representatives of the working team that participated in the trial.

  5. 浅析运用计量经济模型的缺陷%A optimal stimulating method to calculate the VAR of credit risk of loan Analysis of drawbacks of econometric models

    Institute of Scientific and Technical Information of China (English)

    李志强; 詹锋

    2003-01-01

    At present, Econometric models have found a wide range of applications on economic research.However their reliability can be easily understand by intend or unintended abuses. No doubt, econometricmodels have the advantage of revealing the numerical relationships on economic phenomena, but thereexist certain fatal flaws on these econometric models, which if not treated properly will lead to unexpectedmistakes in economic forecast or analysis. In this article some common flaws in econmetric models areinvestigated and some useful measures are suggested for their improvement.

  6. The Gravity Model on EU Countries – An Econometric Approach

    Directory of Open Access Journals (Sweden)

    Megi Marku

    2014-10-01

    Full Text Available Foreign Direct Investment, play a huge role in the growth of the global businesses. They can provide to a firm, new markets where the firm can operate, new marketing channels,manufacturing facilities, access to technology and to the products, and they also provide techniques and funds previously unknown. For the host country, this source of technologies, capital, processes, techniques, and managerial skills can provide an important impetus to the economic development of the country. Through this study, it has been analyzed how FDI are affected by the distance and by the economic size of the country.Given that such a gravity model (size and distance on FDI already exists, this research has examined particularly the impact of these two factors on FDI of the EU member states.Through an econometric model it has been examined how economic size and distance affect foreign investment of EU leading countries in some of the world states and whichof two factors have had the greatest impact. The hypothesis raised in this paper is related to the fact that the gravity model with its factors is extremely important for the volume ofFDI, the size playing a more important role than the distance. By using statistical judgment and econometric analysis it has been explored if the hypothesis rose above, is statistically valid or not. From the model resulted that the impact of the size coefficient on FDI is 0.0042 million Euros, while the distance coefficient is – 0.36 millions. It shows that with the increase of GDP and the distance between countries, FDI increase and decreaserespectively with the above coefficients. According to statistical methods of control, economic size was a more determinant factor than the distance, giving us the idea that in recent years with the increased role of globalization the importance of distance hassignificantly decreased. In this paper it is recommended the gravity models should consider a lot other factors besides size and

  7. ECONOMETRIC MODELING OF THE DYNAMICS OF VOLUMES HYDROCARBONS OF SMALL OIL AND GAS ENTERPRISES

    Directory of Open Access Journals (Sweden)

    GORLOV A.V.

    2015-01-01

    Full Text Available In this paper investigates the principles of functioning of small oil and gas enterprises of Russia. The basic characteristics and socio-economic tasks performed by the small oil and gas enterprises. Made correlation and regression analysis, a result of which the pair correlation coefficients between the indicator of development of small oil and gas enterprises (volumes hydrocarbons and the factors that characterize the work environment of their operation; built regressions, describing the process of development of small oil and gas enterprises. With a view to forecasting the development of small oil and gas enterprises built production function of Cobb-Douglas and selected econometric model, has good predictive properties. Made predictive calculations dynamics of volumes hydrocarbons of small oil and gas enterprises on formulating scenarios for the planning period (2015-2016 years.

  8. With string model to time series forecasting

    OpenAIRE

    Pinčák, Richard; Bartoš, Erik

    2015-01-01

    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 ma...

  9. Empirical spatial econometric modelling of small scale neighbourhood

    Science.gov (United States)

    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.

  10. Macroeconomic Forecasts in Models with Bayesian Averaging of Classical Estimates

    Directory of Open Access Journals (Sweden)

    Piotr Białowolski

    2012-03-01

    Full Text Available The aim of this paper is to construct a forecasting model oriented on predicting basic macroeconomic variables, namely: the GDP growth rate, the unemployment rate, and the consumer price inflation. In order to select the set of the best regressors, Bayesian Averaging of Classical Estimators (BACE is employed. The models are atheoretical (i.e. they do not reflect causal relationships postulated by the macroeconomic theory and the role of regressors is played by business and consumer tendency survey-based indicators. Additionally, survey-based indicators are included with a lag that enables to forecast the variables of interest (GDP, unemployment, and inflation for the four forthcoming quarters without the need to make any additional assumptions concerning the values of predictor variables in the forecast period.  Bayesian Averaging of Classical Estimators is a method allowing for full and controlled overview of all econometric models which can be obtained out of a particular set of regressors. In this paper authors describe the method of generating a family of econometric models and the procedure for selection of a final forecasting model. Verification of the procedure is performed by means of out-of-sample forecasts of main economic variables for the quarters of 2011. The accuracy of the forecasts implies that there is still a need to search for new solutions in the atheoretical modelling.

  11. Benchmarking Judgmentally Adjusted Forecasts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); L.P. de Bruijn (Bert)

    2017-01-01

    textabstractMany publicly available macroeconomic forecasts are judgmentally adjusted model-based forecasts. In practice, usually only a single final forecast is available, and not the underlying econometric model, nor are the size and reason for adjustment known. Hence, the relative weights given

  12. Benchmarking judgmentally adjusted forecasts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); L.P. de Bruijn (Bert)

    2015-01-01

    markdownabstractMany publicly available macroeconomic forecasts are judgmentally-adjusted model-based forecasts. In practice usually only a single final forecast is available, and not the underlying econometric model, nor are the size and reason for adjustment known. Hence, the relative weights

  13. Benchmarking judgmentally adjusted forecasts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); L.P. de Bruijn (Bert)

    2015-01-01

    markdownabstractMany publicly available macroeconomic forecasts are judgmentally-adjusted model-based forecasts. In practice usually only a single final forecast is available, and not the underlying econometric model, nor are the size and reason for adjustment known. Hence, the relative weights give

  14. Fractional Differencing Modeling and Forecasting of Eurocurrency Deposit Rates

    OpenAIRE

    John Barkoulas; Baum, Christopher F

    1996-01-01

    We investigate the low frequency properties of three- and six- month rates for Eurocurrency deposits denominated in eight major currencies with specific emphasis on fractional dynamics. Using the fractional integration testing procedure suggested by Geweke and Porter-Hudak (1983), we find that several of the Eurocurrency deposit rates are fractionally integrated processes with long memory. These findings have important implications for econometric modeling, forecasting, and cointegration test...

  15. Regional energy demand projection of the industrial sector through a comprehensive model of econometrics basis; Projecao da demanda energetica regional do setor industrial atraves de um modelo abrangente, de base econometrica

    Energy Technology Data Exchange (ETDEWEB)

    Bajay, Sergio V.; Silva Walter, Arnaldo C. [Universidade Estadual de Campinas, SP (Brazil). Dept. de Engenharia Mecanica

    1984-12-31

    This paper describes an econometrics-based model developed for energy demand forecasting in the industrial sector. The option for the market-shares approach allows a more effective treatment of the inter-fuel substitution. The model also deals explicitly with problems like: effects of conservation policies market penetration of new technologies and changes in the preferences of consumers. (author). 8 refs

  16. Forecast model of safety economy contribution rate of China

    Institute of Scientific and Technical Information of China (English)

    LIU Li-jun; SHI Shi-liang

    2005-01-01

    It is the rational and exact computation of the safety economy contribution rate that has the far-reaching realistic meaning to the improvement of society cognition to safety and the investment to the nation safety and the national macro-safety decision-makings. The accurate function between safety inputs and outputs was obtained through a founded econometric model. Then the forecasted safety economy contribution rate is 3.01% and the forecasted ratio between safety inputs and outputs is 1:1.81 in China in 2005. And the model accords with the practice of China and the results are satisfying.

  17. Econometric causality

    OpenAIRE

    Heckman, James J.

    2008-01-01

    This paper presents the econometric approach to causal modeling. It is motivated by policy problems. New causal parameters are defined and identified to address specific policy problems. Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective (agent) evaluations as well as objective outcomes. Anticipated and realized subjective and objective outcomes are distinguished. Models for simultaneous causality are developed. The paper ...

  18. Duration Modeling in Undergraduate Econometrics Curriculum via Excel

    Directory of Open Access Journals (Sweden)

    Ismail H. Genc

    2004-01-01

    Full Text Available The main goal of an Econometrics course is to prepare students for more advanced levels of study and make them able to cope with the empirical problems they are likely to face. In recent years, there has been a tendency to purge merely theoretical topics from the econometric textbooks by including only time honored methods. Survival analysis, however, has so far failed to attract the attention of textbook writers. I conjecture that the general perception of the issue as an advanced topic as well as software barriers have led to this attitude. In this study, I work out an Excel file to outline the fundamental concepts of duration analysis at an introductory level. The file so generated may also be used by practitioners in appropriate cases with minimal learning cost. As such, I hope to dissipate the myth of difficulty associated with this subject matter.

  19. Disequilibrium econometrics. An application to modelling of the natural gas market in the USA

    Energy Technology Data Exchange (ETDEWEB)

    Barret, C.

    1990-08-01

    Econometrics methods applicable to limited dependent variable models are presented. Qualitative models are briefly reported, TOBIT models and disequilibrium models are developed. The different formulations are used for simulating the gas market in the USA. Evolution and regulations of this market are reported and an approach by disequilibrium is developed.

  20. Econometric Models, Methodology and Trends regarding public debt and external debt

    Directory of Open Access Journals (Sweden)

    Gheorghe Săvoiu

    2013-10-01

    Full Text Available Statistically-mathematically describing few econometric models as variables, this article approaches the impact and trends regarding public debt and external debt. Conceptually and practically analyzing the evolution of indicators, there are identified specific trends in the economy of Romania, some characteristic models are being parametrised and tested.

  1. Essays on financial econometrics : modeling the term structure of interest rates

    NARCIS (Netherlands)

    Bouwman, Kees Evert

    2008-01-01

    This dissertation bundles five studies in financial econometrics that are related to the theme of modeling the term structure of interest rates. The main contribution of this dissertation is a new arbitrage-free term structure model that is applied in an empirical analysis of the US term structure.

  2. Essays on financial econometrics : modeling the term structure of interest rates

    NARCIS (Netherlands)

    Bouwman, Kees Evert

    2008-01-01

    This dissertation bundles five studies in financial econometrics that are related to the theme of modeling the term structure of interest rates. The main contribution of this dissertation is a new arbitrage-free term structure model that is applied in an empirical analysis of the US term structure.

  3. An alternative to the standard spatial econometric approaches in hedonic house price models

    DEFF Research Database (Denmark)

    von Graevenitz, Kathrine; Panduro, Toke Emil

    2015-01-01

    Omitted, misspecified, or mismeasured spatially varying characteristics are a cause for concern in hedonic house price models. Spatial econometrics or spatial fixed effects have become popular ways of addressing these concerns. We discuss the limitations of standard spatial approaches to hedonic...

  4. An alternative to the standard spatial econometric approaches in hedonic house price models

    DEFF Research Database (Denmark)

    Veie, Kathrine Lausted; Panduro, Toke Emil

    Hedonic models are subject to spatially correlated errors which are a symptom of omitted spatial variables, mis-specification or mismeasurement. Methods have been developed to address this problem through the use of spatial econometrics or spatial fixed effects. However, often spatial correlation...

  5. Forecasting in marketing

    OpenAIRE

    Franses, Philip Hans

    2004-01-01

    textabstractWith the advent of advanced data collection techniques, there is an increased interest in using econometric models to support decisions in marketing. Due to the sometimes specific nature of variables in marketing, the discipline uses econometric models that are rarely, if ever, used elsewhere. This chapter deals with techniques to derive forecasts from these models. Due to the intrinsic non-linear nature of these models, these techniques draw heavliy on simulation techniques.

  6. Forecasting in marketing

    OpenAIRE

    Franses, Philip Hans

    2004-01-01

    textabstractWith the advent of advanced data collection techniques, there is an increased interest in using econometric models to support decisions in marketing. Due to the sometimes specific nature of variables in marketing, the discipline uses econometric models that are rarely, if ever, used elsewhere. This chapter deals with techniques to derive forecasts from these models. Due to the intrinsic non-linear nature of these models, these techniques draw heavliy on simulation techniques.

  7. Econometric Methodology of Monopolization Process Evaluation

    Directory of Open Access Journals (Sweden)

    Dmitrijs Skoruks

    2014-06-01

    Full Text Available The research “Econometric Methodology of Monopolization Process Evaluation” gives a perspective description of monopolization process’ nature, occurrence source, development procedure and internal conjuncture specifics, as well as providing an example of modern econometrical method application within a unified framework of market competition analysis for the purpose of conducting a quantitative competition evaluation on an industry level for practical use in both private and public sectors. The main question of the aforementioned research is the definition and quantitative analysis of monopolization effects in modern day globalized markets, while con- structing an empirical model of the econometric analysis, based on the use of in- ternational historical experience of monopoly formations standings, with the goal of introducing a further development scheme for the use of both econometrical and statistical instruments in line with the forecasting and business research need of enterprises and regulatory functions of the public sector. The current research uses a vast variety of monopolization evaluation ratios and their econometrical updates on companies that are involved in the study procedure in order to detect and scallar measure their market monopolizing potential, based on the implemented acquired market positions, turnover shares and competition policies.

  8. Modelling the world oil market: Assessment of a quarterly econometric model

    Energy Technology Data Exchange (ETDEWEB)

    Dees, Stephane [European Central Bank, Kaiserstrasse 29, 60311 Frankfurt (Germany)]. E-mail: stephane.dees@ecb.int; Karadeloglou, Pavlos [European Central Bank, Kaiserstrasse 29, 60311 Frankfurt-am-Main (Germany); Kaufmann, Robert K. [Center for Energy and Environmental Studies, Boston University (United States); Sanchez, Marcelo [European Central Bank, Kaiserstrasse 29, 60311 Frankfurt (Germany)

    2007-01-15

    This paper describes a structural econometric model of the world oil market that can be used to analyse oil market developments and risks. Oil demand depends on domestic economic activity and the real price of oil. Oil supply for non-OPEC producers, based on competitive behaviours, is constrained by geological and institutional conditions. Oil prices are determined by a 'price rule' that includes market conditions and OPEC behaviour. Policy simulations indicate that oil demand and non-OPEC supply are rather inelastic to changes in price, while OPEC decisions about quota and capacity utilisation have a significant, immediate impact on oil prices.

  9. The use of econometric models for long-term policies: A critical view

    Directory of Open Access Journals (Sweden)

    Luigi Spaventa

    2013-09-01

    Full Text Available The present work provides a first appraisal of the contribution that econometric models can make to long-run economic policy. Rather than a detailed exposition or critique of the individual models, a more general analysis is provided. The author first outlines the econometric models considered, all of which provide a more or less accurate description of the state of the economic system in some future moment of time. In order to appraise the reliability of the information provided by the models their main relationships are examined from the point of view of their simultaneous working in the context of the whole model. Finally, the author examines what utility the more or less reliable information provided by formal models purporting to represent the hypothetical or desired situation of the economy at a certain date can have for economic policy decisions. The author concludes that there is no available econometric model from which reliable and useful information can be obtained for planning and for economic policy. Moreover, no reliable and useful indication is ever likely to be obtained from so-called consistency models.

  10. A model for Long-term Industrial Energy Forecasting (LIEF)

    Energy Technology Data Exchange (ETDEWEB)

    Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)

    1992-02-01

    The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

  11. Applied econometrics with R

    CERN Document Server

    Kleiber, Christian

    2008-01-01

    Offers an introduction to the R system for users with a background in economics. This book covers a variety of regression models, regression diagnostics and robustness issues, the nonlinear models of microeconomics, time series and time series econometrics.

  12. A model for Long-term Industrial Energy Forecasting (LIEF)

    Energy Technology Data Exchange (ETDEWEB)

    Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))

    1992-02-01

    The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

  13. Study on Game-theoretic Econometric Models%博弈计量经济模型研究

    Institute of Scientific and Technical Information of China (English)

    王文举; 王方军

    2014-01-01

    博弈计量经济模型是博弈论与计量经济学的结合,是对博弈模型的计量经济分析。博弈论和计量经济学已成为当今分析经济问题的两种最有力的方法,博弈计量经济模型分析将这两种方法结合在一起,必将使问题的分析以更加符合现实的方式揭示经济活动的内在规律。从静态博弈计量经济模型、动态博弈计量经济模型、序贯博弈计量经济模型三个方面梳理博弈计量经济模型的已有研究,分析现有研究的贡献和不足,并指出进一步研究的方向和思路。%Game-theoretic econometric models are econometric analysis of game models.Econometrics and game theory have become the two current most powerful approaches of analyzing economic problems.Game-theoretic econometric models combining the two approaches will make the analysis of the problems in a more realistic way to reveal the inherent laws of economic activities.This paper reviews the existing researches of game-theoretic econometric models in three aspects,static game-theoretic econometric models,dynamic game-theoretic econometric models and sequential game-theoretic econometric models,analyzes the contributions and the shortcomings of the existing researches,and points out the direction and thinking of further study.

  14. Catastrophe models and the expansion method: A review of issues and an application to the econometric modeling of economic growth

    Directory of Open Access Journals (Sweden)

    Emilio Casetti

    1997-01-01

    Full Text Available Many negative reactions to Catastrophe Theory have been triggered by overly simplistic applications unintended and unsuited for statistical-econometric estimation, inference, and testing. In this paper it is argued that stochastic catastrophe models constructed using the Expansion Method hold the most promise to widen the acceptance of Catastrophe Theory by analytically oriented scholars in the social sciences and elsewhere. The paper presents a typology of catastrophe models, and demonstrates the construction and estimation of an econometric expanded cusp catastrophe model of economic growth.

  15. Robustness in econometrics

    CERN Document Server

    Sriboonchitta, Songsak; Huynh, Van-Nam

    2017-01-01

    This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.

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

    Energy Technology Data Exchange (ETDEWEB)

    Berndt, E.R.; Fraumeni, B.M.; Hudson, E.A.; Jorgenson, D.W.; Stoker, T.M.

    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 are developed in the report on the model specification).

  17. NYHOPS Forecast Model Results

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.

  18. An econometric model on bilateral trade in education using an augmented gravity model

    Directory of Open Access Journals (Sweden)

    Christina Tay

    2014-05-01

    Full Text Available Purpose: Trade in education has become one of the most important trades for many economies. Yet, studies of education as a trade are scant owing to the conventional view of it being non-tradable. The purpose of this paper is to econometrically investigate trade in education using a nexus of international trade theories and the gravity model, one of the most widely used models in international trade in goods that has been scantly investigated on in studies on trade in education.Design/methodology/approach: A panel data analysis is broken down for 21 exporting countries and 50 importing countries, covering 1050 observations using new UNESCO database. A number of determinants of international trade including wealth of exporter & importer, domestic capacity of exporter & importer, transport costs, common religion, common language and trade restrictiveness of the importer are empirically tested on bilateral trade flows in education. An econometric model is formulated to test determinants of trade in education using an augmented gravity model.Findings: The augmented gravity model used in this study explains with high significance the determinants of trade in education including wealth of exporter & importer, domestic capacity of exporter & importer, transport costs, common religion, common language and trade restrictiveness of the importer.Research limitations/implications: Taking a macroscopic view of education as a trade may give us a myopic view of the elements important to determine what students or parents of students as well as institutions are concerned with. Nevertheless, the nexus of international trade theories and the gravity model used in this study that are largely and traditionally used on trade in goods and services, but scantly used in trade in education have been found to be highly significant and relevant in trade in education. Future studies on macro-level of analysis involving trade in education could include other determinants of

  19. With string model to time series forecasting

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  1. An Econometric Model for SINOPEC Stock Price Tendency on Domestic Securities Market

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A time series analysis method was used to establish an econometric model for SINOPEC'S stock price tendency on the domestic securities market under the background of sharp oil price rises in recent years. The model was proven to be a non-stationary time series and unit root process, as tested with the Dickey-Fuller method, and the result of a practical case showed that this model could well reflect SINOPEC stock price tendency on the securities market of China. It would be a guide for research and prediction of stock price tendency.

  2. Forecasting with Dynamic Regression Models

    CERN Document Server

    Pankratz, Alan

    2012-01-01

    One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

  3. A revised econometric model of the domestic pallet market

    Science.gov (United States)

    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...

  4. Descriptive documentation for New Mexico electricity econometric final demand model

    Energy Technology Data Exchange (ETDEWEB)

    Baxter, J.D.; Ben-David, S.

    1981-01-01

    A mathematical model is developed for computing consumption and residential electric power demands for New Mexico. Factors considered in developing the model included: number of electric utility customers, past consumption data; household devices using electric power and their energy efficiencies; climatic conditions; and power costs. (LCL)

  5. Growth Econometrics

    OpenAIRE

    JOHNSON, Paul; Steven N. Durlauf; Temple, Johnathan R. W.

    2004-01-01

    This paper provides a survey and synthesis of econometric tools that have been employed to study economic growth. While these tools range across a variety of statistical methods, they are united in the common goals of first, identifying interesting contemporaneous patterns in growth data and second, drawing inferences on long-run economic outcomes from cross-section and temporal variation in growth. We describe the main stylized facts that have motivated the development of growth econometrics...

  6. EU practices of education staff planning (Application of econometric models

    Directory of Open Access Journals (Sweden)

    Mr.Sc. Sahit Surdulli

    2011-12-01

    The research results indicated that there is great interdependence between the economic growth norm in the country in one hand and the attained educational results on the economy of knowledge on the other hand. The interdependence between the number of workers and their qualification structure and the results attained in the education field in models, was expressed through equations. The Empiriev model as a concrete model for planning the necessary education cadre for certain levels of economic development is based on the basic model of Tinbergen – Bos. The coefficient values of regression reflect the form and intensity of interdependency between the number of students per million inhabitants and the national income per capita.

  7. Some Econometric Results for the Blanchard-Watson Bubble Model

    DEFF Research Database (Denmark)

    Johansen, Soren; Lange, Theis

    The purpose of the present paper is to analyse a simple bubble model suggested by Blanchard and Watson. The model is defined by y(t) =s(t)¿y(t-1)+e(t), t=1,…,n, where s(t) is an i.i.d. binary variable with p=P(s(t)=1), independent of e(t) i.i.d. with mean zero and finite variance. We take ¿>1 so...

  8. Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models

    DEFF Research Database (Denmark)

    David, M.; Ramahatana, F.; Trombe, Pierre-Julien;

    2016-01-01

    of the grid-connected storage systems. If numerous methods for forecasting the mean of the solar irradiance were recently developed, there are only few works dedicated to the evaluation of prediction intervals associated to these point forecasts. Time series of solar irradiance and more specifically of clear...... sky index show some similarities with that of financial time series. The aim of this paper is to assess the performances of a commonly used combination of two linear models (ARMA and GARCH) in econometrics in order to provide probabilistic forecasts of solar irradiance. In addition, a recursive...... estimation of the parameters of the models has been set up in order to provide a framework that can be applied easily in an operational context. A comprehensive testing procedure has been used to assess both point forecasts and probabilistic forecasts. Using only the past records of the solar irradiance...

  9. Some Econometric Results for the Blanchard-Watson Bubble Model

    DEFF Research Database (Denmark)

    Johansen, Soren; Lange, Theis

    The purpose of the present paper is to analyse a simple bubble model suggested by Blanchard and Watson. The model is defined by y(t) =s(t)¿y(t-1)+e(t), t=1,…,n, where s(t) is an i.i.d. binary variable with p=P(s(t)=1), independent of e(t) i.i.d. with mean zero and finite variance. We take ¿>1 so...... is whether a bubble model with infinite variance can create the long swings, or persistence, which are observed in many macro variables. We say that a variable is persistent if its autoregressive coefficient ¿(n) of y(t) on y(t-1), is close to one. We show that the estimator of ¿(n) converges to ¿p...

  10. An Econometric Model of Healthcare Demand With Nonlinear Pricing.

    Science.gov (United States)

    Kunz, Johannes S; Winkelmann, Rainer

    2016-04-04

    From 2004 to 2012, the German social health insurance levied a co-payment for the first doctor visit in a calendar quarter. We develop a new model for estimating the effect of such a co-payment on the individual number of visits per quarter. The model combines a one-time increase in the otherwise constant hazard rate determining the timing of doctor visits with a difference-in-differences strategy to identify the reform effect. An extended version of the model accounts for a mismatch between reporting period and calendar quarter. Using data from the German Socio-Economic Panel, we do not find an effect of the co-payment on demand for doctor visits. Copyright © 2016 John Wiley & Sons, Ltd.

  11. Occupational gender segregation: index measurement and econometric modeling.

    Science.gov (United States)

    Watts, M

    1998-11-01

    Empirical studies of gender segregation by occupation must be founded on rigorous measurement procedures. There appears to be a consensus that any index used in the analysis of time-series or international cross-section employment data must be either margin-free or decomposable to yield a margin-free component. On the other hand, Charles and Grusky (1995) advocate the use of multiplicative log models from which a margin-free odds ratio can be derived. In this paper, I contrast the construction and interpretation of the index of dissimilarity and the Karmel-MacLachlan index with the multiplicative modeling of gender segregation and the associated log index.

  12. Compensation and Promotion Models: A New Econometrics Approach

    Science.gov (United States)

    2010-01-29

    m itjmw 1 M itj itjm m m w v    1,1itj    1,1mv The Information Theoretic, General Maximum Entropy (IT-GME) Model: (Maximizing the joint...that may contradict economic intuition/theory. • From a practical point of view, a number of researchers show that the multivariate (or bivariate ... entropies of the signal and noise subject to the linear cross moments, First Order Markov conditions, and the requirements that w are proper

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

    Science.gov (United States)

    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.

  14. SUSTAINING PADDY SELF-SUFFICIENCY AND LAND DEMANDS IN SABAH, MALAYSIA: A STRUCTURAL PADDY AND RICE ECONOMETRIC MODEL ANALYSIS

    Directory of Open Access Journals (Sweden)

    Wong Kelly_Kai_Seng

    2017-01-01

    Full Text Available The objective of this study is to construct an econometric commodity model in order to forecast the long term rice production performance of the state of Sabah, Malaysia. The baseline projection shows that the Sabah rice self-sufficiency is estimated to achieve approximately38% in the next 10 years due to the scarcity of the suitable land bank allocate for paddy cultivation. In order to achieve 60% of targeted rice self-sufficiency level (SSL, the size of land for paddy cultivation must be increased in Sabah. Based on the scenario simulation projection result, the expansion of paddy cultivation area will contribute a positively to the industrial rice production and consequently achieving the expected 60% of SSL by the end of 2024. In a nutshell, the state government of Sabah possess state autonomy on the land management, thus the state government plays a significant key role on promoting the local rice self-sufficiency level in the long-term period

  15. Electricity generation modeling and photovoltaic forecasts in China

    Science.gov (United States)

    Li, Shengnan

    With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979--2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.

  16. Econometric Model Estimation and Sensitivity Analysis of Inputs for Mandarin Production in Mazandaran Province of Iran

    Directory of Open Access Journals (Sweden)

    Majid Namdari

    2011-05-01

    Full Text Available This study examines energy consumption of inputs and output used in mandarin production, and to find relationship between energy inputs and yield in Mazandaran, Iran. Also the Marginal Physical Product (MPP method was used to analyze the sensitivity of energy inputs on mandarin yield and returns to scale of econometric model was calculated. For this purpose, the data were collected from 110 mandarin orchards which were selected based on random sampling method. The results indicated that total energy inputs were 77501.17 MJ/ha. The energy use efficiency, energy productivity and net energy of mandarin production were found as 0.77, 0.41 kg/MJ and -17651.17 MJ/ha. About 41% of the total energy inputs used in mandarin production was indirect while about 59% was direct. Econometric estimation results revealed that the impact of human labor energy (0.37 was found the highest among the other inputs in mandarin production. The results also showed that direct, indirect and renewable and non-renewable, energy forms had a positive and statistically significant impact on output level. The results of sensitivity analysis of the energy inputs showed that with an additional use of 1 MJ of each of the human labor, farmyard manure and chemical fertilizers energy would lead to an increase in yield by 2.05, 1.80 and 1.26 kg, respectively. The results also showed that the MPP value of direct and renewable energy were higher.

  17. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

    OpenAIRE

    Fay, D; Ringwood, John; Condon, M.

    2004-01-01

    Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...

  18. Behavioral Econometrics for Psychologists

    DEFF Research Database (Denmark)

    Andersen, Steffen; Harrison, Glenn W.; Lau, Morten

    We make the case that psychologists should make wider use of structural econometric methods. These methods involve the development of maximum likelihood estimates of models, where the likelihood function is tailored to the structural model. In recent years these models have been developed...

  19. Econometric model of intraurban location of emitters and receptors of industrial air pollution

    Energy Technology Data Exchange (ETDEWEB)

    Santini, D. J.; Braid, R.

    1977-02-01

    An econometric model of air pollution for an intraurban location (the Chicago area) is constructed and estimated. The model treats employment and population as simultaneously determined. Exogenous variables are selected to represent transportation infrastructure investments resulting primarily from federal and state decisions. The exogenous variables account for the relative services provided by highways, commuter railroads, rail rapid transit, waterways, and airports. The employment location equations appear to be considerably more successful than those in previous studies. These equations indicate that waterway availability constrains the locational options of most major industrial air polluters; that highway accessibility is a more influential factor in industrial than services location choices; that rail rapid transit accessibility is more important to services than industrial locations; and that major airports attract light industrial development. The success of the employment location equations reflects the importance of disaggregating intraurban modes of transport and of adding to urban location models the local effects of interurban modes of transport such as water and air.

  20. Non-Extensive Entropy Econometrics: New Statistical Features of Constant Elasticity of Substitution-Related Models

    Directory of Open Access Journals (Sweden)

    Second Bwanakare

    2014-05-01

    Full Text Available Power-law (PL formalism is known to provide an appropriate framework for canonical modeling of nonlinear systems. We estimated three stochastically distinct models of constant elasticity of substitution (CES class functions as non-linear inverse problem and showed that these PL related functions should have a closed form. The first model is related to an aggregator production function, the second to an aggregator utility function (the Armington and the third to an aggregator technical transformation function. A q-generalization of K-L information divergence criterion function with a priori consistency constraints is proposed. Related inferential statistical indices are computed. The approach leads to robust estimation and to new findings about the true stochastic nature of this class of nonlinear—up until now—analytically intractable functions. Outputs from traditional econometric techniques (Shannon entropy, NLLS, GMM, ML are also presented.

  1. Econometrics of risk

    CERN Document Server

    Kreinovich, Vladik; Sriboonchitta, Songsak; Suriya, Komsan

    2015-01-01

    This edited book contains several state-of-the-art papers devoted to econometrics of risk. Some papers provide theoretical analysis of the corresponding mathematical, statistical, computational, and economical models. Other papers describe applications of the novel risk-related econometric techniques to real-life economic situations. The book presents new methods developed just recently, in particular, methods using non-Gaussian heavy-tailed distributions, methods using non-Gaussian copulas to properly take into account dependence between different quantities, methods taking into account imprecise ("fuzzy") expert knowledge, and many other innovative techniques. This versatile volume helps practitioners to learn how to apply new techniques of econometrics of risk, and researchers to further improve the existing models and to come up with new ideas on how to best take into account economic risks.

  2. Commercial demand for energy: a disaggregated approach. [Model validation for 1970-1975; forecasting to 2000

    Energy Technology Data Exchange (ETDEWEB)

    Jackson, J.R.; Cohn, S.; Cope, J.; Johnson, W.S.

    1978-04-01

    This report describes the structure and forecasting accuracy of a disaggregated model of commercial energy use recently developed at Oak Ridge National Laboratory. The model forecasts annual commercial energy use by ten building types, five end uses, and four fuel types. Both economic (utilization rate, fuel choice, capital-energy substitution) and technological factors (equipment efficiency, thermal characteristics of buildings) are explicitly represented in the model. Model parameters are derived from engineering and econometric analysis. The model is then validated by simulating commercial energy use over the 1970--1975 time period. The model performs well both with respect to size of forecast error and ability to predict turning points. The model is then used to evaluate the energy-use implications of national commercial buildings standards based on the ASHRAE 90-75 recommendations. 10 figs., 12 tables, 14 refs.

  3. Econometric modelling of unemployment in Serbia during period 2008-2013

    Directory of Open Access Journals (Sweden)

    Anić Aleksandra

    2014-01-01

    Full Text Available The purpose of the paper is to econometrically exploit the characteristics of unemployment in Serbia upon the start of the 2008 economic crisis. The methodological framework is based on the cointegrated vector autoregressive model that consists of the following macroeconomic variables: unemployment rate, prices, nominal wages and nominal exchange rate. These variables are unit-root processes and their relationship is examined within the multivariate cointegrated time series set-up. Following the deductive modelling approach, we reached the specification that explains unemployment rate by real wages. The results show the negative consequences of the economic crisis to the labour market, with an extremely high increase in the unemployment rate. Strong negative impact of real wages on unemployment rate is additionally confirmed by its dynamic effects throughout the impulse response function.

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

    Science.gov (United States)

    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.

  5. ECONOMETRIC MODEL OF FIRM’S VALUE IN LIQUID MARKET: CASE OF INDONESIA

    Directory of Open Access Journals (Sweden)

    Putu Agus Ardiana

    2012-11-01

    Full Text Available The research aims to investigate variables affecting Tobin’s Q which represents the value of public  companies listed on LQ45 Index on the Indonesia Stock Exchange by developing a  BLUE(Best Linear UnbiasedEstimators econometric model for cross-sectional data of 2007, 2008, and 2009 as well as panel data. The models vary across different data but there are important findings to note. Public companies listed  on LQ45 Index have experienced overliquidity problem during the period of observation leading to a decline in firm’s value. In addition, those public companies have low financial risk so they have chance to  increase their debts especially long-term debts.

  6. Environmental forecasting and turbulence modeling

    Science.gov (United States)

    Hunt, J. C. R.

    This review describes the fundamental assumptions and current methodologies of the two main kinds of environmental forecast; the first is valid for a limited period of time into the future and over a limited space-time ‘target’, and is largely determined by the initial and preceding state of the environment, such as the weather or pollution levels, up to the time when the forecast is issued and by its state at the edges of the region being considered; the second kind provides statistical information over long periods of time and/or over large space-time targets, so that they only depend on the statistical averages of the initial and ‘edge’ conditions. Environmental forecasts depend on the various ways that models are constructed. These range from those based on the ‘reductionist’ methodology (i.e., the combination of separate, scientifically based, models for the relevant processes) to those based on statistical methodologies, using a mixture of data and scientifically based empirical modeling. These are, as a rule, focused on specific quantities required for the forecast. The persistence and predictability of events associated with environmental and turbulent flows and the reasons for variation in the accuracy of their forecasts (of the first and second kinds) are now better understood and better modeled. This has partly resulted from using analogous results of disordered chaotic systems, and using the techniques of calculating ensembles of realizations, ideally involving several different models, so as to incorporate in the probabilistic forecasts a wider range of possible events. The rationale for such an approach needs to be developed. However, other insights have resulted from the recognition of the ordered, though randomly occurring, nature of the persistent motions in these flows, whose scales range from those of synoptic weather patterns (whether storms or ‘blocked’ anticyclones) to small scale vortices. These eigen states can be predicted

  7. New Employment Forecasts. Hotel and Catering Industry 1988-1993.

    Science.gov (United States)

    Measurement for Management Decision, Ltd., London (England).

    Econometric forecasting models were used to forecast employment levels in the hotel and catering industry in Great Britain through 1993 under several different forecasting scenarios. The growth in employment in the hotel and catering industry over the next 5 years is likely to be broadly based, both across income levels of domestic consumers,…

  8. New Employment Forecasts. Hotel and Catering Industry 1988-1993.

    Science.gov (United States)

    Measurement for Management Decision, Ltd., London (England).

    Econometric forecasting models were used to forecast employment levels in the hotel and catering industry in Great Britain through 1993 under several different forecasting scenarios. The growth in employment in the hotel and catering industry over the next 5 years is likely to be broadly based, both across income levels of domestic consumers,…

  9. Econometric Based Modeling of Population Growth under Socio-cultual Constraints

    CERN Document Server

    Ausloos, Marcel; Herteliu, Claudiu; Ileanu, Bogdan Vasile

    2015-01-01

    There are many constraints on population growth or decay in a country: several are of socio-economic origins. Sometimes cultual constraints also exist: sexual intercourse is banned in various religions, during Nativity and Lent fasting periods. We analyzed data consisting of registered daily birth records for very long (35,429 points) time series and many (24,947,061) babies in Romania between 1905 and 2001 (97 years). The data was obtained from the 1992 and 2002 censuses. We grouped the population into two categories (Eastern Orthodox and Non-Orthodox) in order to distinguish cultual constraints. We performed extensive data analysis in a comparative manner for both groups. From such a long time series data analysis, it seems that the Lent fast has a more drastic effect than the Nativity fast over baby conception within the Eastern Orthodox population, thereby differently increasing the population ratio. Thereafter, we developed and tested econometric models where the dependent variable is the baby conception...

  10. Robust Econometrics

    OpenAIRE

    Čίžek, Pavel; Härdle, Wolfgang Karl

    2006-01-01

    Econometrics often deals with data under, from the statistical point of view, non-standard conditions such as heteroscedasticity or measurement errors and the estimation methods need thus be either adopted to such conditions or be at least insensitive to them. The methods insensitive to violation of certain assumptions, for example insensitive to the presence of heteroscedasticity, are in a broad sense referred to as robust (e.g., to heteroscedasticity). On the other hand, there is also a mor...

  11. Modelling and Forecasting Multivariate Realized Volatility

    DEFF Research Database (Denmark)

    Halbleib, Roxana; Voev, Valeri

    2011-01-01

    This paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes. The approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast. We provide an empirical appl...

  12. Electricity Price Forecasting using Sale and Purchase Curves: The X-Model

    OpenAIRE

    Florian Ziel; Rick Steinert

    2015-01-01

    Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usually done in time series or data mining approaches, we model and utilize its true source: the sale and purchase curves of the electricity exchange. We will refer to this new model ...

  13. Selection bias in species distribution models: An econometric approach on forest trees based on structural modeling

    Science.gov (United States)

    Martin-StPaul, N. K.; Ay, J. S.; Guillemot, J.; Doyen, L.; Leadley, P.

    2014-12-01

    Species distribution models (SDMs) are widely used to study and predict the outcome of global changes on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of applications on forest trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8km). We also compared the outputs of the SSDM with outputs of a classical SDM (i.e. Biomod ensemble modelling) in terms of bioclimatic response curves and potential distributions under current climate and climate change scenarios. The shapes of the bioclimatic response curves and the modelled species distribution maps differed markedly between SSDM and classical SDMs, with contrasted patterns according to species and spatial resolutions. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents

  14. The Standard Model in the history of the Natural Sciences, Econometrics, and the social sciences

    Science.gov (United States)

    Fisher, W. P., Jr.

    2010-07-01

    In the late 18th and early 19th centuries, scientists appropriated Newton's laws of motion as a model for the conduct of any other field of investigation that would purport to be a science. This early form of a Standard Model eventually informed the basis of analogies for the mathematical expression of phenomena previously studied qualitatively, such as cohesion, affinity, heat, light, electricity, and magnetism. James Clerk Maxwell is known for his repeated use of a formalized version of this method of analogy in lectures, teaching, and the design of experiments. Economists transferring skills learned in physics made use of the Standard Model, especially after Maxwell demonstrated the value of conceiving it in abstract mathematics instead of as a concrete and literal mechanical analogy. Haavelmo's probability approach in econometrics and R. Fisher's Statistical Methods for Research Workers brought a statistical approach to bear on the Standard Model, quietly reversing the perspective of economics and the social sciences relative to that of physics. Where physicists, and Maxwell in particular, intuited scientific method as imposing stringent demands on the quality and interrelations of data, instruments, and theory in the name of inferential and comparative stability, statistical models and methods disconnected theory from data by removing the instrument as an essential component. New possibilities for reconnecting economics and the social sciences to Maxwell's sense of the method of analogy are found in Rasch's probabilistic models for measurement.

  15. The Standard Model in the history of the Natural Sciences, Econometrics, and the social sciences

    Energy Technology Data Exchange (ETDEWEB)

    Fisher, W P Jr, E-mail: william@livingcapitalmetrics.co [LivingCapitalMetrics.com, 5252 Annunciation St, New Orleans, Louisiana 70115 (United States)

    2010-07-01

    In the late 18th and early 19th centuries, scientists appropriated Newton's laws of motion as a model for the conduct of any other field of investigation that would purport to be a science. This early form of a Standard Model eventually informed the basis of analogies for the mathematical expression of phenomena previously studied qualitatively, such as cohesion, affinity, heat, light, electricity, and magnetism. James Clerk Maxwell is known for his repeated use of a formalized version of this method of analogy in lectures, teaching, and the design of experiments. Economists transferring skills learned in physics made use of the Standard Model, especially after Maxwell demonstrated the value of conceiving it in abstract mathematics instead of as a concrete and literal mechanical analogy. Haavelmo's probability approach in econometrics and R. Fisher's Statistical Methods for Research Workers brought a statistical approach to bear on the Standard Model, quietly reversing the perspective of economics and the social sciences relative to that of physics. Where physicists, and Maxwell in particular, intuited scientific method as imposing stringent demands on the quality and interrelations of data, instruments, and theory in the name of inferential and comparative stability, statistical models and methods disconnected theory from data by removing the instrument as an essential component. New possibilities for reconnecting economics and the social sciences to Maxwell's sense of the method of analogy are found in Rasch's probabilistic models for measurement.

  16. Novel grey forecast model and its application

    Institute of Scientific and Technical Information of China (English)

    丁洪发; 舒双焰; 段献忠

    2003-01-01

    The advancement of grey system theory provides an effective analytic tool for power system load fore-cast. All kinds of presently available grey forecast models can be well used to deal with the short-term load fore-cast. However, they make big errors for medium or long-term load forecasts, and the load that does not satisfythe approximate exponential increasing law in particular. A novel grey forecast model that is capable of distin-guishing the increasing law of load is adopted to forecast electric power consumption (EPC) of Shanghai. Theresults show that this model can be used to greatly improve the forecast precision of EPC for a secondary industryor the whole society.

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

    Directory of Open Access Journals (Sweden)

    Soocheol Lee

    2012-01-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  19. A Simple Fuzzy Time Series Forecasting Model

    DEFF Research Database (Denmark)

    Ortiz-Arroyo, Daniel

    2016-01-01

    In this paper we describe a new first order fuzzy time series forecasting model. We show that our automatic fuzzy partitioning method provides an accurate approximation to the time series that when combined with rule forecasting and an OWA operator improves forecasting accuracy. Our model does...... not attempt to provide the best results in comparison with other forecasting methods but to show how to improve first order models using simple techniques. However, we show that our first order model is still capable of outperforming some more complex higher order fuzzy time series models....

  20. Econometrics Network Information Society

    Directory of Open Access Journals (Sweden)

    Aleksandr Bogomolov

    2014-04-01

    Full Text Available The paper proposes to overcome the shortcomings of classical econometrics , noted economists and practitioners, scientists , using the capabilities of modern computer and information technology, network approach to the description of models and methodology agent - based modeling . The authors propose to introduce the concept of a network and the network of natural economic agent , the latter is a random event , the probability of which can be determined based on Bayesian belief networks .

  1. Windpower econometrics

    Energy Technology Data Exchange (ETDEWEB)

    Kuemmel, B. [KVL, AgroEcology, Taastrup (Denmark)

    1999-12-01

    Interpolating into the future is necessary to gain an idea of e.g. the share that renewable energy can cover over the next decades. While some research groups have applied guestimates when deriving renewable scenarios, this paper present results of a simple econometric study of the Danish windpower sector. In this article the recent, historic development of the Danish windpower industry which indicates a steep learning curve is analysed, and the the trend over the next 10 years is extrapolated. Furthermore an attempt is made to analyse what the recent economic difficulties of one major player in the Danish windpower industry may mean for the future price and revenue trends and for the displaced CO{sub 2} emissions.

  2. LAND – PRICE DETERMINANTS USING THE SPATIAL ECONOMETRICS MODELING IN THE MOLDAVIAN REAL ESTATE MARKET

    Directory of Open Access Journals (Sweden)

    Anatol RACUL

    2012-01-01

    Full Text Available The purpose of this paper is to determine the factors which influence the land market in Republic of Moldova. The paper aims to discover the determinants for land pricing using the spatial econometrics modeling, as it is widely used when the spatial component is present. The country’s agricultural economy combined with the interest of international organizations and limited data availability directed the focus of this empirical study towards land for agricultural purposes. The factors which determine the land market (for agricultural purposes in Republic of Moldova are mainly related to economic characteristics of land, such as field productivity, the position on the local landscape (characterized by angle and soil quality, proximity to local or national roads (due to storage and transportation reasons, and economic characteristics of owners. Also, another important role in land market price creation is the pressure of urban space to transform land for agricultural use close to cities and villages in spaces for industrial or residential purposes. This is characterized by the financial pressure from the urban centers which has become significant in land transactions.

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

    Science.gov (United States)

    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.

  4. Forecasting with nonlinear time series models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...... applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic...

  5. Development of an expert system in econometrics. Application to energy demand modelling; Construction d`un systeme expert en econometrie. Application a la demande d`energie

    Energy Technology Data Exchange (ETDEWEB)

    Fauveau, A.

    1993-05-03

    The proper use of econometric softwares requires both statistical and economic skills. The main objective of this thesis is to provide the users of regression programs with assistance in the process of regression analysis by means of expert system technology. We first built an expert system providing general econometric strategy. The running principle of the program is based on a ``estimation - hypothesis check - specification improvement`` cycle. Its econometric expertise is a consistent set of statistical technics and analysis rules for estimating one equation. Then, we considered the inclusion of the economic knowledge required to produce a consistent analysis; we focused on energy demand modelling. The economic knowledge base is independent from the econometric rules, this allow us to update it easily. (author).

  6. Real Time Econometrics

    OpenAIRE

    Pesaran, M. Hashem; Timmermann, Allan

    2004-01-01

    This paper considers the problems facing decision makers using econometric models in real time. It identifies the key stages involved and highlights the role of automated systems in reducing the effect of data snooping. It sets out many choices that researchers face in construction of automated systems and discusses some of the possible ways advanced in the literature for dealing with them. The role of feedbacks from the decision maker?s actions to the data generating process is also discusse...

  7. Forecasting with periodic autoregressive time series models

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1999-01-01

    textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

  8. Forecasting with periodic autoregressive time series models

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1999-01-01

    textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

  9. Convolution copula econometrics

    CERN Document Server

    Cherubini, Umberto; Mulinacci, Sabrina

    2016-01-01

    This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.

  10. Forecasting Interest Rates and Inflation

    DEFF Research Database (Denmark)

    Chun, Albert Lee

    the best overall for short horizon forecasts of short to medium term yields and inflation. Econometric models with shrinkage perform the best over longer horizons and maturities. Aggregating over a larger set of analysts improves inflation surveys while generally degrading interest rates surveys. We...

  11. Forecasting models for national economic planning

    CERN Document Server

    Heesterman, A R G

    1972-01-01

    This book is about the specification of linear econometric models, and for this reason some important related fields have been deliberately omitted. I did not want to discuss the problems of parameter-estimation, at least not in any detail, as there are other books on these problems written by specialized statisticians. This book is about the models them­ selves and macro-economic models in particular. A second related sub­ ject is the policy decision that can be made with the help of a model. While I did write a chapter on policy decisions, I limited myself to some extent because of my views on planning as such. The logical approach to this problem is in terms of mathematical programming, but our models and our ideas about the policies we want are too crude for its effective utilisation. A realistic formulation of the problem should involve non­ linearities in an essential way, the models I consider (and most existing models) are linear. At the present state of econometrics, I do not really believe in suc...

  12. Modelling and forecasting WIG20 daily returns

    DEFF Research Database (Denmark)

    Amado, Cristina; Silvennoinen, Annestiina; Terasvirta, Timo

    of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity....

  13. Midway Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Midway Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite...

  14. Bermuda Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Bermuda Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  15. SPREADSHEET MODELING AS A TOOL FOR INTERACTIVE LEARNING OF BASIC CONCEPTS OF ECONOMETRICS

    Directory of Open Access Journals (Sweden)

    Ekaterina B. Gribanova

    2016-01-01

    Full Text Available The article describes how to develop interactive graphical tools to illustrate the basic concepts of econometrics: linear regression, index of determination, autocorrelation, heteroscedasticity, etc. the Implementation is made in the spreadsheet program Excel using the simulation method and can be useful in teaching discipline

  16. Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets

    Directory of Open Access Journals (Sweden)

    Erie Febrian

    2014-11-01

    Full Text Available Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in different volatility approximations. The concern becomes more complicated when one tries to use the forecasting for asset distribution and risk management purposes in the linked regional markets. This paper aims at observing the effectiveness of the contending models of statistical and econometric volatility forecasting in the three South-east Asian prominent capital markets, i.e. STI, KLSE, and JKSE. In this paper, we evaluate eleven different models based on two classes of evaluation measures, i.e. symmetric and asymmetric error statistics, following Kumar's (2006 framework. We employ 10-year data as in sample and 6-month data as out of sample to construct and test the models, consecutively. The resulting superior methods, which are selected based on the out of sample forecasts and some evaluation measures in the respective markets, are then used to assess the markets cointegration. We find that the best volatility forecasting models for JKSE, KLSE, and STI are GARCH (2,1, GARCH(3,1, and GARCH (1,1, respectively. We also find that international portfolio investors cannot benefit from diversification among these three equity markets as they are cointegrated.

  17. Operational, regional-scale, chemical weather forecasting models in Europe

    NARCIS (Netherlands)

    Kukkonen, J.; Balk, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.H.; Poupkou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.

    2011-01-01

    Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed

  18. Demand forecast model based on CRM

    Science.gov (United States)

    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.

  19. Grey-Markov Model for Road Accidents Forecasting

    Institute of Scientific and Technical Information of China (English)

    李相勇; 严余松; 蒋葛夫

    2003-01-01

    In order to improve the forecasting precision of road accidents, by introducing Markov chains forecasting method, a grey-Markov model for forecasting road accidents is established based on grey forecasting method. The model combines the advantages of both grey forecasting method and Markov chains forecasting method, overcomes the influence of random fluctuation data on forecasting precision and widens the application scope of the grey forecasting. An application example is conducted to evaluate the grey-Markov model, which shows that the precision of the grey-Markov model is better than that of grey model in forecasting road accidents.

  20. Application of hydrologic forecast model.

    Science.gov (United States)

    Hua, Xu; Hengxin, Xue; Zhiguo, Chen

    2012-01-01

    In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasible and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model.

  1. Long-term Industrial Energy Forecasting (LIEF) model (18-sector version)

    Energy Technology Data Exchange (ETDEWEB)

    Ross, M.H. (Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Physics); Thimmapuram, P.; Fisher, R.E.; Maciorowski, W. (Argonne National Lab., IL (United States))

    1993-05-01

    The new 18-sector Long-term Industrial Energy Forecasting (LIEF) model is designed for convenient study of future industrial energy consumption, taking into account the composition of production, energy prices, and certain kinds of policy initiatives. Electricity and aggregate fossil fuels are modeled. Changes in energy intensity in each sector are driven by autonomous technological improvement (price-independent trend), the opportunity for energy-price-sensitive improvements, energy price expectations, and investment behavior. Although this decision-making framework involves more variables than the simplest econometric models, it enables direct comparison of an econometric approach with conservation supply curves from detailed engineering analysis. It also permits explicit consideration of a variety of policy approaches other than price manipulation. The model is tested in terms of historical data for nine manufacturing sectors, and parameters are determined for forecasting purposes. Relatively uniform and satisfactory parameters are obtained from this analysis. In this report, LIEF is also applied to create base-case and demand-side management scenarios to briefly illustrate modeling procedures and outputs.

  2. Long-term Industrial Energy Forecasting (LIEF) model (18-sector version)

    Energy Technology Data Exchange (ETDEWEB)

    Ross, M.H. [Univ. of Michigan, Ann Arbor, MI (US). Dept. of Physics; Thimmapuram, P.; Fisher, R.E.; Maciorowski, W. [Argonne National Lab., IL (US)

    1993-05-01

    The new 18-sector Long-term Industrial Energy Forecasting (LIEF) model is designed for convenient study of future industrial energy consumption, taking into account the composition of production, energy prices, and certain kinds of policy initiatives. Electricity and aggregate fossil fuels are modeled. Changes in energy intensity in each sector are driven by autonomous technological improvement (price-independent trend), the opportunity for energy-price-sensitive improvements, energy price expectations, and investment behavior. Although this decision-making framework involves more variables than the simplest econometric models, it enables direct comparison of an econometric approach with conservation supply curves from detailed engineering analysis. It also permits explicit consideration of a variety of policy approaches other than price manipulation. The model is tested in terms of historical data for nine manufacturing sectors, and parameters are determined for forecasting purposes. Relatively uniform and satisfactory parameters are obtained from this analysis. In this report, LIEF is also applied to create base-case and demand-side management scenarios to briefly illustrate modeling procedures and outputs.

  3. Forecasting elections in Europe: Synthetic models

    Directory of Open Access Journals (Sweden)

    Michael S. Lewis-Beck

    2015-01-01

    Full Text Available Scientific work on national election forecasting has become most developed for the United States case, where three dominant approaches can be identified: Structuralists, Aggregators, and Synthesizers. For European cases, election forecasting models remain almost exclusively Structuralist. Here we join together structural modeling and aggregate polling results, to form a hybrid, which we label a Synthetic Model. This model contains a political economy core, to which poll numbers are added (to tap omitted variables. We apply this model to a sample of three Western European countries: Germany, Ireland, and the United Kingdom. This combinatory strategy appears to offer clear forecasting gains, in terms of lead and accuracy.

  4. NAVO NCOM Relocatable Model: Fukushima Regional Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary NCOM Relocatable 1km forecast model for Fukushima Region. USERS ARE REMINDED TO USE THE FUKUSHIMA 1KM NCOM DATA WITH CAUTION. THE MODEL WAS INITIATED ON...

  5. Modelling and Forecasting Multivariate Realized Volatility

    DEFF Research Database (Denmark)

    Chiriac, Roxana; Voev, Valeri

    This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions....... We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies...

  6. Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Andersen, Torben G.; Diebold, Francis X.

    A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff...... bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements in a simple...... but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications...

  7. Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Andersen, Torben G.; Diebold, Francis X.

    -Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury......A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff...... but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications...

  8. Forecasting elections in Europe: Synthetic models

    OpenAIRE

    Michael S. Lewis-Beck; Ruth Dassonneville

    2015-01-01

    Scientific work on national election forecasting has become most developed for the United States case, where three dominant approaches can be identified: Structuralists, Aggregators, and Synthesizers. For European cases, election forecasting models remain almost exclusively Structuralist. Here we join together structural modeling and aggregate polling results, to form a hybrid, which we label a Synthetic Model. This model contains a political economy core, to which poll numbers are added (to ...

  9. Combining SKU-level sales forecasts from models and experts

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Legerstee (Rianne)

    2009-01-01

    textabstractWe study the performance of SKU-level sales forecasts which linearly combine statistical model forecasts and expert forecasts. Using a large and unique database containing model forecasts for monthly sales of various pharmaceutical products and forecasts given by about fifty experts, we

  10. ECONOMETRIC MODELLING OD THE INFLUENCE OF LAKE WATER QUALITY CHANGES ON FISHING ECONOMY

    Directory of Open Access Journals (Sweden)

    Marek Antoni Ramczyk

    2017-06-01

    Full Text Available The econometric model can be a precise instrument for the analysis of the impact of the natural environment's degradation on fishing economy. This paper aims at analysing the influence of the water quality changes in lake Charzykowskie on the fishing economy. This dissertation present the results of a research on the lake water pollution's impact on fishing economy. The economic-ecological models have been constructed, explaining the changes of economic effects of the lake fishery in the conditions of an increasing water pollution in the epilimnion on the example of the catch of Rutilus rutilus, Abramis brama, Blicca bjoerkna, Coregonus albula, Coregonus lavaretus, Anguilla anguilla and Esox lucius in Lake Charzykowskie. Performed empirical research looked into the influence of the environmental factors on the size of fish catch. Calculations and analysis show clearly that though the habitat factors do influence the catch size of each studied fish species, they do it with different intensity and in various combinations. Both lake water quality and climate factors changes cause measurable effects on fishing industry of lake Charzykowskie. Among all the examined Rutilus rutilus, Abramis brama and Blicca bjoerkna the highest environmental requirements concerning water quality has Blicca bjoerkna. Whereas Abramis brama has slightly higher environmental requirements than Rutilus rutilus. Empirical calculations showed as well that Coregonus albula and Coregonus lavaretus have considerably higher water cleanness requirements than Rutilus rutilus, Abramis brama and Blicca bjoerkna. While when talking about Rutilus rutilus, Abramis brama and Blicca bjoerkna, most water characteristics still rather stimulated these species' development, when it comes to Coregonus albula and Coregonus lavaretus, in general they suppressed their development. The model has also proved quite high habitat requierements of Anquilla anquilla and correctness of the thesis that

  11. A Forecast Model for Unemployment by Education

    DEFF Research Database (Denmark)

    Tranæs, Torben; Larsen, Anders Holm; Groes, Niels

    1994-01-01

    We present a dynamic forecast model for the labour market: demand for labour by education and the distribution of labour by education among industries are determined endogenously with overall demand by industry given exogenously. The model is derived from a simple behavioural equation based...... on a strong relationship between the “strength” in the struggle for jobs of an educational group, and the change in relative supply. This relationship proves to be significant in the data. Furthermore, when used to forecast employment by education on real data, the model predicts reasonably well even...... for educational groups, where the initial forecast year is a change point for unemployment....

  12. The Stability of Currency Systems in East Asia --Quantitative Analysis Using a Multi-Country Macro-Econometric Model--

    OpenAIRE

    Koichiro Kamada

    2009-01-01

    The purpose of this paper is to examine the stability of East Asian financial and currency systems, using the multi-country macro-econometric model constructed by Kamada and Takagawa (2005) to depict economic interdependence in the Asian-Pacific region. The highly-developed system of the international production network in the East Asian region was not only the driving force behind the "East Asian miracle," but also, as seen in the "Asian currency crisis," worked as a platform whereby local e...

  13. 预测型稳健回归模型及其实证分析%Forecasting Robust Regression Model and the Experimental Analysis

    Institute of Scientific and Technical Information of China (English)

    王新军; 黄守坤

    2004-01-01

    The econometrics model which is established by traditional ordinary least square mainly reveals the long-term and average relations among economical variables. In the forecasting application, it couldn't distinguish near future and far future influence. As a result, its forecasting application can' t reveal the short-term waved changes of variables. By using the method that is applied in M-estimation of robust regression to deal with outliers, we set up forecasting robust regression model. Our purpose is to add factors that economical variables are influenced by time sequence into regression model in its forecasting application. By empirical analysis, we also test and verify that this method indeed heighten the model's forecasting precision.

  14. Nambe Pueblo Water Budget and Forecasting model.

    Energy Technology Data Exchange (ETDEWEB)

    Brainard, James Robert

    2009-10-01

    This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

  15. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-02-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  16. Modelling and forecasting Australian domestic tourism

    OpenAIRE

    2006-01-01

    In this paper, we model and forecast Australian domestic tourism demand. We use a regression framework to estimate important economic relationships for domestic tourism demand. We also identify the impact of world events such as the 2000 Sydney Olympics and the 2002 Bali bombings on Australian domestic tourism. To explore the time series nature of the data, we use innovation state space models to forecast the domestic tourism demand. Combining these two frameworks, we build innovation state s...

  17. Municipal water consumption forecast accuracy

    Science.gov (United States)

    Fullerton, Thomas M.; Molina, Angel L.

    2010-06-01

    Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.

  18. The AviaDem forecasting model: illustration of a forecasting case at Amsterdam Schiphol Airport

    NARCIS (Netherlands)

    Veldhuis, J.; Lieshout, R.

    2010-01-01

    The paper describes an aviation market forecasting model which focuses on market forecasts for airports. Most forecasting models in use today assess aviation trends resulting from macroeconomic trends. The model described in this paper has this feature built in, but the added value of this model is

  19. A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

    Directory of Open Access Journals (Sweden)

    Lianhui Li

    2015-12-01

    Full Text Available Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.

  20. Modeling and forecasting petroleum futures volatility

    Energy Technology Data Exchange (ETDEWEB)

    Sadorsky, Perry [York Univ., Schulich School of Business, Toronto, ON (Canada)

    2006-07-15

    Forecasts of oil price volatility are important inputs into macroeconometric models, financial market risk assessment calculations like value at risk, and option pricing formulas for futures contracts. This paper uses several different univariate and multivariate statistical models to estimate forecasts of daily volatility in petroleum futures price returns. The out-of-sample forecasts are evaluated using forecast accuracy tests and market timing tests. The TGARCH model fits well for heating oil and natural gas volatility and the GARCH model fits well for crude oil and unleaded gasoline volatility. Simple moving average models seem to fit well in some cases provided the correct order is chosen. Despite the increased complexity, models like state space, vector autoregression and bivariate GARCH do not perform as well as the single equation GARCH model. Most models out perform a random walk and there is evidence of market timing. Parametric and non-parametric value at risk measures are calculated and compared. Non-parametric models outperform the parametric models in terms of number of exceedences in backtests. These results are useful for anyone needing forecasts of petroleum futures volatility. (author)

  1. Predicting the local impacts of energy development: a critical guide to forecasting methods and models

    Energy Technology Data Exchange (ETDEWEB)

    Sanderson, D.; O' Hare, M.

    1977-05-01

    Models forecasting second-order impacts from energy development vary in their methodology, output, assumptions, and quality. As a rough dichotomy, they either simulate community development over time or combine various submodels providing community snapshots at selected points in time. Using one or more methods - input/output models, gravity models, econometric models, cohort-survival models, or coefficient models - they estimate energy-development-stimulated employment, population, public and private service needs, and government revenues and expenditures at some future time (ranging from annual to average year predictions) and for different governmental jurisdictions (municipal, county, state, etc.). Underlying assumptions often conflict, reflecting their different sources - historical data, comparative data, surveys, and judgments about future conditions. Model quality, measured by special features, tests, exportability and usefulness to policy-makers, reveals careful and thorough work in some cases and hurried operations with insufficient in-depth analysis in others.

  2. Post Processing Numerical Weather Prediction Model Rainfall Forecasts for Use in Ensemble Streamflow Forecasting in Australia

    Science.gov (United States)

    Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.

    2012-12-01

    Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.

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

    Science.gov (United States)

    Katikireddi, Srinivasa V; Bond, Lyndal; Hilton, Shona

    2014-06-01

    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. 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. 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. 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. © The Author 2013. Published by Oxford University Press on behalf of the European Public Health Association.

  4. A broad model for demand forecasting of gasoline and fuel alcohol; Um modelo abrangente para a projecao das demandas de gasolina e alcool carburante

    Energy Technology Data Exchange (ETDEWEB)

    Buonfiglio, Antonio [PETROBRAS, Paulinia, SP (Brazil). Dept. Industrial; Bajay, Sergio Valdir [Universidade Estadual de Campinas, SP (Brazil). Faculdade de Engenharia Mecanica

    1991-12-31

    Formulating a broad, mixed: econometric/end-use, demand forecasting model for gasoline and fuel alcohol is the main objective of this work. In the model, the gasoline and hydrated alcohol demands are calculated as the corresponding products if their fleet by the average car mileage, divided by the average specific mileage. Several simulations with the proposed forecasting model are carried out, within the context of alternative scenarios for the development of these competing fuels in the Brazilian market. (author) 4 refs., 1 fig., 3 tabs.

  5. Improving the teaching of econometrics

    Directory of Open Access Journals (Sweden)

    David F. Hendry

    2016-12-01

    Full Text Available We recommend a major shift in the Econometrics curriculum for both graduate and undergraduate teaching. It is essential to include a range of topics that are still rarely addressed in such teaching, but are now vital for understanding and conducting empirical macroeconomic research. We focus on a new approach to macro-econometrics teaching, since even undergraduate econometrics courses must include analytical methods for time series that exhibit both evolution from stochastic trends and abrupt changes from location shifts, and so confront the “non-stationarity revolution”. The complexity and size of the resulting equation specifications, formulated to include all theory-based variables, their lags and possibly non-linear functional forms, as well as potential breaks and rival candidate variables, places model selection for models of changing economic data at the centre of teaching. To illustrate our proposed new curriculum, we draw on a large UK macroeconomics database over 1860–2011. We discuss how we reached our present approach, and how the teaching of macro-econometrics, and econometrics in general, can be improved by nesting so-called “theory-driven” and “data-driven” approaches. In our methodology, the theory-model’s parameter estimates are unaffected by selection when the theory is complete and correct, so nothing is lost, whereas when the theory is incomplete or incorrect, improved empirical models can be discovered from the data. Recent software like Autometrics facilitates both the teaching and the implementation of econometrics, supported by simulation tools to examine operational performance, designed to be feasibly presented live in the classroom.

  6. A Bayesian Combination Forecasting Model for Retail Supply Chain Coordination

    Directory of Open Access Journals (Sweden)

    W.J. Wang

    2014-04-01

    Full Text Available Retailing plays an important part in modern economic development, and supply chain coordination is the research focus in retail operations management. This paper reviews the collaborative forecasting process within the framework of the collaborative planning, forecasting and replenishment of retail supply chain. A Bayesian combination forecasting model is proposed to integrate multiple forecasting resources and coordinate forecasting processes among partners in the retail supply chain. Based on simulation results for retail sales, the effectiveness of this combination forecasting model is demonstrated for coordinating the collaborative forecasting processes, resulting in an improvement of demand forecasting accuracy in the retail supply chain.

  7. Electricity demand load forecasting of the Hellenic power system using an ARMA model

    Energy Technology Data Exchange (ETDEWEB)

    Pappas, S.Sp. [ASPETE - School of Pedagogical and Technological Education Department of Electrical Engineering Educators N. Heraklion, 141 21 Athens (Greece); Ekonomou, L.; Chatzarakis, G.E.; Skafidas, P.D. [ASPETE-School of Pedagogical and Technological Education, Department of Electrical Engineering Educators, N. Heraklion, 141 21 Athens (Greece); Karampelas, P. [Hellenic American University, IT Department, 12 Kaplanon Str., 106 80 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24 100 Kalamata (Greece); Katsikas, S.K. [University of Piraeus, Department of Technology Education and Digital Systems, 150 Androutsou St., 18 532 Piraeus (Greece)

    2010-03-15

    Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts. (author)

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

    Science.gov (United States)

    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

  9. Econometrics Applying to the Interdisciplinary Studies

    Directory of Open Access Journals (Sweden)

    Romeo-Victor Ionescu

    2015-10-01

    Full Text Available The paper deals with the idea that econometrics represents a useful instrument for thye economic analysis, even at regional level. According to the labor market conditions, econometrics allows several techniques in order to estimate the structural parameters of an a priori specified system of simultaneous stochastic equations. Moreover, the econometric approach highlights the labor system function as to maximize the employees’ number and labor demand, or to minimize the unemployment rate. A distinct part of the analysis covers the regional econometric approach in connection to regional location and optimum models. The main conclusion of the analysis is that econometrics is able to force the knowledge limits not only in regional economics. The analysis and the conclusions are supported by pertinent diagrams and mathematical relations.

  10. Modelling and Forecasting Multivariate Realized Volatility

    DEFF Research Database (Denmark)

    Chiriac, Roxana; Voev, Valeri

    . We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model's forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies...

  11. Pollen Forecast and Dispersion Modelling

    Science.gov (United States)

    Costantini, Monica; Di Giuseppe, Fabio; Medaglia, Carlo Maria; Travaglini, Alessandro; Tocci, Raffaella; Brighetti, M. Antonia; Petitta, Marcello

    2014-05-01

    The aim of this study is monitoring, mapping and forecast of pollen distribution for the city of Rome using in-situ measurements of 10 species of common allergenic pollens and measurements of PM10. The production of daily concentration maps, associated to a mobile phone app, are innovative compared to existing dedicated services to people who suffer from respiratory allergies. The dispersal pollen is one of the most well-known causes of allergic disease that is manifested by disorders of the respiratory functions. Allergies are the third leading cause of chronic disease and it is estimated that tens millions of people in Italy suffer from it. Recent works reveal that during the last few years there was a progressive increase of affected subjects, especially in urban areas. This situation may depend: on the ability to transport of pollutants, on the ability to react between pollutants and pollen and from a combination of other irritants, existing in densely populated and polluted urban areas. The methodology used to produce maps is based on in-situ measurements time series relative to 2012, obtained from networks of air quality and pollen stations in the metropolitan area of Rome. The monitoring station aerobiological of University of Rome "Tor Vergata" is located at the Department of Biology. The instrument used to pollen monitoring is a volumetric sampler type Hirst (Hirst 1952), Model 2000 VPPS Lanzoni; the data acquisition is carried out as reported in Standard UNI 11008:2004 - "Qualità dell'aria - Metodo di campionamento e conteggio dei granuli pollinici e delle spore fungine aerodisperse" - the protocol that describes the procedure for measuring of the concentration of pollen grains and fungal spores dispersed into the atmosphere, and reported in the "Manuale di gestione e qualità della R.I.M.A" (Travaglini et. al. 2009). All 10 allergenic pollen are monitored since 1996. At Tor Vergata university is also operating a meteorological station (SP2000, CAE

  12. Forecasting Models in the State Education System

    Directory of Open Access Journals (Sweden)

    Gintautas DZEMYDA

    2003-04-01

    Full Text Available This paper presents model-based assessment and forecasting of the Lithuanian education system in the period of 2001-2010. In order to obtain satisfactory forecasting results, constructing of models used for these aims should be grounded on some interactive data mining. Data mining of data stored in the system of the Lithuanian teacher's database and of data from other sources representing the state of education system and the demographic changes in Lithuania was used. The models cover the estimation of data quality in the databases, the analysis of flow of teachers and pupils, the clustering of schools, the model of dynamics of pedagogical staff and pupils, and the quality analysis of teachers. The main results of forecasting and integrated analysis of the Lithuanian teachers' database with other data reflecting the state of the education system and demographic changes in Lithuania are presented.

  13. Causal inference in econometrics

    CERN Document Server

    Kreinovich, Vladik; Sriboonchitta, Songsak

    2016-01-01

    This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

  14. Forecasting and Analysis of Agricultural Product Logistics Demand in Tibet Based on Combination Forecasting Model

    Institute of Scientific and Technical Information of China (English)

    Wenfeng; YANG

    2015-01-01

    Over the years,the logistics development in Tibet has fallen behind the transport. Since the opening of Qinghai-Tibet Railway in2006,the opportunity for development of modern logistics has been brought to Tibet. The logistics demand analysis and forecasting is a prerequisite for regional logistics planning. By establishing indicator system for logistics demand of agricultural products,agricultural product logistics principal component regression model,gray forecasting model,BP neural network forecasting model are built. Because of the single model’s limitations,quadratic-linear programming model is used to build combination forecasting model to predict the logistics demand scale of agricultural products in Tibet over the next five years. The empirical analysis results show that combination forecasting model is superior to single forecasting model,and it has higher precision,so combination forecasting model will have much wider application foreground and development potential in the field of logistics.

  15. Three essays in econometric theory

    NARCIS (Netherlands)

    Gan, Zhuojiong

    2015-01-01

    This thesis consists of three essays in econometric theory. In the first essay, he considers a prediction problem with a large number of predictors. He improves the prediction precision of the standard factor model by allowing some variables to have idiosyncratic factors that are relevant for predic

  16. ECONOMETRICS AND REALITY

    OpenAIRE

    Kevin Hoover

    2003-01-01

    Starting with a realist ontology the economic methodologist, Tony Lawson, argues that econometrics is a failed project. Apparently more sympathetic to econometrics, the philosopher of science, Nancy Cartwright, again from a realist perspective, nonetheless argues for conditions of applicability that are so stringent that she must seriously doubt the usefulness of econometrics. In this paper, I reconsider Lawson''s and Cartwright''s analyses and argue that realism supports rather than undermin...

  17. Forecasting Exchange Rates with Mixed Models

    Directory of Open Access Journals (Sweden)

    Laura Maria Badea

    2013-06-01

    Full Text Available Gaining accuracy in exchange rate forecasting applications provides true benefits for financial activities. Supported today by the advancements in computing power, machine learning techniques provide good alternatives to traditional time series estimation methods. Very approached in time series forecasting are Artificial Neural Networks (ANNs which offer robust results and allow a flexible data manipulation. When integrating both, the “white-box” feature of conventional methods and the complexity of machine learning techniques, forecasting models perform even better in terms of generated errors. In this study, input variables (independent variables are selected using an ARIMA technique and are further employed in differently configured multilayered feed-forward neural networks using Broyden-Fletcher-Goldfarb-Shanno (BFGS optimization algorithm to perform predictions on EUR/RON and CHF/RON exchange rates. Results in terms of mean squared error highlight good results when using mixed models.

  18. Nonparametric model validations for hidden Markov models with applications in financial econometrics.

    Science.gov (United States)

    Zhao, Zhibiao

    2011-06-01

    We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

  19. Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-05-01

    Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.

  20. Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-09-01

    Full Text Available Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short

  1. Towards Disaggregate Dynamic Travel Forecasting Models

    Institute of Scientific and Technical Information of China (English)

    Moshe Ben-Akiva; Jon Bottom; Song Gao; Haris N. Koutsopoulos; Yang Wen

    2007-01-01

    The authors argue that travel forecasting models should be dynamic and disaggregate in their representation of demand, supply, and supply-demand interactions, and propose a framework for such models.The proposed framework consists of disaggregate activity-based representation of travel choices of individual motorists on the demand side integrated with disaggregate dynamic modeling of network performance,through vehicle-based traffic simulation models on the supply side. The demand model generates individual members of the population and assigns to them socioeconomic characteristics. The generated motorists maintain these characteristics when they are loaded on the network by the supply model. In an equilibrium setting, the framework lends itself to a fixed-point formulation to represent and resolve demand-supply interactions. The paper discusses some of the remaining development challenges and presents an example of an existing travel forecasting model system that incorporates many of the proposed elements.

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

    Science.gov (United States)

    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.

  3. Forecasting the Unit Cost of a Product with Some Linear Fuzzy Collaborative Forecasting Models

    Directory of Open Access Journals (Sweden)

    Toly Chen

    2012-10-01

    Full Text Available Forecasting the unit cost of every product type in a factory is an important task. However, it is not easy to deal with the uncertainty of the unit cost. Fuzzy collaborative forecasting is a very effective treatment of the uncertainty in the distributed environment. This paper presents some linear fuzzy collaborative forecasting models to predict the unit cost of a product. In these models, the experts’ forecasts differ and therefore need to be aggregated through collaboration. According to the experimental results, the effectiveness of forecasting the unit cost was considerably improved through collaboration.

  4. Forecasting characteristic earthquakes in a minimalist model

    DEFF Research Database (Denmark)

    Vázquez-Prada, M.; Pacheco, A.; González, Á.

    2003-01-01

    Using error diagrams, we quantify the forecasting of characteristic-earthquake occurence in a recently introduced minimalist model. Initially we connect the earthquake alarm at a fixed time after the occurence of a characteristic event. The evaluation of this strategy leads to a one-dimensional n...

  5. Applications products of aviation forecast models

    Science.gov (United States)

    Garthner, John P.

    1988-01-01

    A service called the Optimum Path Aircraft Routing System (OPARS) supplies products based on output data from the Naval Oceanographic Global Atmospheric Prediction System (NOGAPS), a model run on a Cyber-205 computer. Temperatures and winds are extracted from the surface to 100 mb, approximately 55,000 ft. Forecast winds are available in six-hour time steps.

  6. Forecast Master Program case studies: Final report

    Energy Technology Data Exchange (ETDEWEB)

    Engle, R.; Granger, C.; Ramanathan, R. (ed.)

    1987-04-01

    This report presents a number of case studies using the computer software package FORECAST MASTER (FM). The series studied and forecast are, aggregate monthly California Electricity Sales, system energy demand data from Ontario Hydro, peak demand data for the residential and commercial customers of Georgia Power Company, Massachusetts Electric commercial sales, Narragansett Electric commercial sales, average and peak demand using Georgia Power Company data. A variety of methods have been studied by each of the contributing authors; trend line fitting, exponential smoothing, Box-Jenkins univariate forecasting, vector autoregression, state space modeling, dynamic econometric models including time-varying parameters and general order serial correlation corrections. Thus both the data sets and the modeling/forecasting methodologies are varied. A number of conclusions emerge from these case studies: FM provides a powerful set of tools to aid a utility forecaster, a great deal of caution should be exercised in pre-processing the data; it can have unintended side effects, diagnostic tests are very useful in econometric models, the Akaike Information Criterion is a useful measure for selecting the best state space model, and state space and econometric approaches both need equal amounts of care in model analysis and presentation.

  7. Accounting for selection bias in species distribution models: An econometric approach on forested trees based on structural modeling

    Science.gov (United States)

    Ay, Jean-Sauveur; Guillemot, Joannès; Martin-StPaul, Nicolas K.; Doyen, Luc; Leadley, Paul

    2015-04-01

    Species distribution models (SDMs) are widely used to study and predict the outcome of global change on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of application on forested trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8 km). We also compared the output of the SSDM with outputs of a classical SDM in term of bioclimatic response curves and potential distribution under current climate. According to the species and the spatial resolution of the calibration dataset, shapes of bioclimatic response curves the modelled species distribution maps differed markedly between the SSDM and classical SDMs. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents a crucial step to account for economic constraints on tree

  8. Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

    Directory of Open Access Journals (Sweden)

    D. L. Shrestha

    2013-05-01

    Full Text Available The quality of precipitation forecasts from four Numerical Weather Prediction (NWP models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT and regional (ACCESS-R NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

  9. Airfreight forecasting methodology and results

    Science.gov (United States)

    1978-01-01

    A series of econometric behavioral equations was developed to explain and forecast the evolution of airfreight traffic demand for the total U.S. domestic airfreight system, the total U.S. international airfreight system, and the total scheduled international cargo traffic carried by the top 44 foreign airlines. The basic explanatory variables used in these macromodels were the real gross national products of the countries involved and a measure of relative transportation costs. The results of the econometric analysis reveal that the models explain more than 99 percent of the historical evolution of freight traffic. The long term traffic forecasts generated with these models are based on scenarios of the likely economic outlook in the United States and 31 major foreign countries.

  10. Mesoscale Modeling, Forecasting and Remote Sensing Research.

    Science.gov (United States)

    remote sensing , cyclonic scale diagnostic studies and mesoscale numerical modeling and forecasting are summarized. Mechanisms involved in the release of potential instability are discussed and simulated quantitatively, giving particular attention to the convective formulation. The basic mesoscale model is documented including the equations, boundary condition, finite differences and initialization through an idealized frontal zone. Results of tests including a three dimensional test with real data, tests of convective/mesoscale interaction and tests with a detailed

  11. EXPENSES FORECASTING MODEL IN UNIVERSITY PROJECTS PLANNING

    Directory of Open Access Journals (Sweden)

    Sergei A. Arustamov

    2016-11-01

    Full Text Available The paper deals with mathematical model presentation of cash flows in project funding. We describe different types of expenses linked to university project activities. Problems of project budgeting that contribute most uncertainty have been revealed. As an example of the model implementation we consider calculation of vacation allowance expenses for project participants. We define problems of forecast for funds reservation: calculation based on methodology established by the Ministry of Education and Science calculation according to the vacation schedule and prediction of the most probable amount. A stochastic model for vacation allowance expenses has been developed. We have proposed methods and solution of the problems that increase the accuracy of forecasting for funds reservation based on 2015 data.

  12. Comment on "Polynomial cointegration tests of anthropogenic impact on global warming" by Beenstock et al. (2012) – some hazards in econometric modelling of climate change

    OpenAIRE

    F. Pretis; Hendry, D.F.

    2013-01-01

    We outline six important hazards that can be encountered in econometric modelling of time-series data, and apply that analysis to demonstrate errors in the empirical modelling of climate data in Beenstock et al. (2012). We show that the claim made in Beenstock et al. (2012) as to the different degrees of integrability of CO2 and temperature is incorrect. In particular, the level of integration is not constant and not intrinsic to the process. Further, we illustrate that the ...

  13. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the islands of Samoa at...

  14. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the island of Guam at...

  15. Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Commonwealth of the Northern...

  16. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 3.5-day hourly forecast for the region surrounding the Hawaiian island of Oahu at...

  17. Modeling Growth Trend and Forecasting Techniques for Vehicular Population in India

    Directory of Open Access Journals (Sweden)

    Kartikeya Jha

    2013-06-01

    Full Text Available Forecasting and estimation of growth in vehicular population is a sine qua non of any major transportation engineering development, requires capturing the past trend and using it to predict the future trend based on qualified assumptions, simulations and models created using explanatory variables. This work attempts to review the in vogue approaches and investigate a more contemporary approach, the Time Series (TS Analysis. Three fundamentally different methods were explored and results from each of these analyses were collated to check for respective levels of accuracy in predicting vehicular population for the same target year. Within the scope of this study and estimation, results obtained from TS Analysis were found to be considerably more accurate than those from Trend Line Analysis and observably better than those from Econometric Analysis. To reinforce these observations and inferences drawn, a second set of analysis was done on more recent input by using AADT data from PeMS, California. Inter alia this was carried out to contrast any statistical improvement observed when doing TS analysis with rich and accurate data. With all the data sets used and locations analyzed for forecasting, the Time Series analysis technique was invariably found to be a potent tool for forecasting.

  18. An Applied Physicist Does Econometrics

    Science.gov (United States)

    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? )

  19. Spatial Econometric Model for Economics Development in Archipelago of Riau, as a Defense System Development in Republic of Indonesia

    Directory of Open Access Journals (Sweden)

    Susanti Linuwih

    2010-08-01

    Full Text Available Province of Archipelago of Riau is a region in Indonesia which is adjacent to Singapore and Malaysia. This province has a great potential conditions diversity and natural resources. Planning on public prosperity improvement is necessary in order to increase loyalty and nationalism to Republic of Indonesia. The aim of this research is to build a spatial econometric model of economic growth in Province of Archipelago of Riau. One of the results shows that in recent 4 years Batam always gives the largest contribution to GRDP in Province of Archipelago of Riau. This can be understood that the contribution is more than 72.0% not only based on GRDP at current prices, but also based on GRDP at constant prices. Economic growth rate in regions in Province of Archipelago of Riau is higher than national economic growth rate. The model fits well because the coefficient of determination R2 is more than 85%. There are only 3 worse models, i.e. based on building construction in Batam (with R2= 59.6%, in Tanjungpinang (with R2=74.0%, and based on transportation and communication in Tanjungpinang (with R2=37.1%.

  20. NEW CAR DEMAND MODELING AND FORECASTING USING BASS DIFFUSION MODEL

    Directory of Open Access Journals (Sweden)

    Zuhaimy Ismail

    2013-01-01

    Full Text Available Forecasting model of new product demand has been developed and applied to forecast new vehicle demand in Malaysia. Since the publication of the Bass model in 1969, innovation of new diffusion theory has sparked considerable research among marketing science scholars, operational researchers and mathematicians. The building of Bass diffusion model for forecasting new product within the Malaysian society is presented in this study. The proposed model represents the spread level of new Proton car among a given set of the society in terms of a simple mathematical function that elapsed since the introduction of the new car. With the limited amount of data available for the new car, a robust Bass model was developed to forecast the sales volume. A procedure of the proposed diffusion model was designed and the parameters were estimated. Results obtained by applying the proposed model and numerical calculation shows that the proposed diffusion model is robust and effective for forecasting demand of new Proton car. The proposed diffusion model is shown to forecast more effectively and accurately even with insufficient previous data on the new product.

  1. Econometric Modelling of the Variations of Norway’s Export Trade across Continents and over Time: The Two-Stage Non-Full Rank Hierarchical Linear Econometric Model Approach

    Directory of Open Access Journals (Sweden)

    Yohannes Yebabe Tesfay

    2015-01-01

    Full Text Available This paper applies the two-stage hierarchical non-full rank linear econometric model to make a deep analysis based on revenue generated from key Norwegian export items over the world’s continents. The model’s ability to analyse the variation of Norway’s export trade gives us the following interesting details: (1 for each continent intra- and intervariation of export items, (2 access to deep knowledge about the characteristics of the Norway’s export items revenue, (3 quantifying the economic importance and sustainability of export items within continents; and finally (4 comparing a given export item economic importance across continents. The results suggest the following important policy implications for Norway. First, Europe is the most important trade partner for Norway. In fact, 81.5% of Norwegian export items are transported to Europe. Second, there is a structural shift in Norwegian exports from North and Central America to Asia and Oceania. Third, the new importance of Asia and Oceania is also emphasized by the 85% increase in export revenues over the period 1988–2012. The trade pattern has changed and trade policy must change accordingly. The analysis has shown that in 2012 there are two important export continents for Norway: Europe and Asia and Oceania.

  2. The Red Sea Modeling and Forecasting System

    KAUST Repository

    Hoteit, Ibrahim

    2015-04-01

    Despite its importance for a variety of socio-economical and political reasons and the presence of extensive coral reef gardens along its shores, the Red Sea remains one of the most under-studied large marine physical and biological systems in the global ocean. This contribution will present our efforts to build advanced modeling and forecasting capabilities for the Red Sea, which is part of the newly established Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). Our Red Sea modeling system compromises both regional and nested costal MIT general circulation models (MITgcm) with resolutions varying between 8 km and 250 m to simulate the general circulation and mesoscale dynamics at various spatial scales, a 10-km resolution Weather Research Forecasting (WRF) model to simulate the atmospheric conditions, a 4-km resolution European Regional Seas Ecosystem Model (ERSEM) to simulate the Red Sea ecosystem, and a 1-km resolution WAVEWATCH-III model to simulate the wind driven surface waves conditions. We have also implemented an oil spill model, and a probabilistic dispersion and larval connectivity modeling system (CMS) based on a stochastic Lagrangian framework and incorporating biological attributes. We are using the models outputs together with available observational data to study all aspects of the Red Sea circulations. Advanced monitoring capabilities are being deployed in the Red Sea as part of the SAMERCK, comprising multiple gliders equipped with hydrographical and biological sensors, high frequency (HF) surface current/wave mapping, buoys/ moorings, etc, complementing the available satellite ocean and atmospheric observations and Automatic Weather Stations (AWS). The Red Sea models have also been equipped with advanced data assimilation capabilities. Fully parallel ensemble-based Kalman filtering (EnKF) algorithms have been implemented with the MITgcm and ERSEM for assimilating all available multivariate satellite and in-situ data sets. We

  3. Stochastic model of forecasting spare parts demand

    Directory of Open Access Journals (Sweden)

    Ivan S. Milojević

    2012-01-01

    hypothesis of the existence of phenomenon change trends, the next step in the methodology of forecasting is the determination of a specific growth curve that describes the regularity of the development in time. These curves of growth are obtained by the analytical representation (expression of dynamic lines. There are two basic stages in the process of expression and they are: - The choice of the type of curve the shape of which corresponds to the character of the dynamic order variation - the determination of the number of values (evaluation of the curve parameters. The most widespread method of forecasting is the trend extrapolation. The basis of the trend extrapolation is the continuing of past trends in the future. The simplicity of the trend extrapolation process, on the one hand, and the absence of other information on the other hand, are the main reasons why the trend extrapolation is used for forecasting. The trend extrapolation is founded on the following assumptions: - The phenomenon development can be presented as an evolutionary trajectory or trend, - General conditions that influenced the trend development in the past will not undergo substantial changes in the future. Spare parts demand forecasting is constantly being done in all warehouses, workshops, and at all levels. Without demand forecasting, neither planning nor decision making can be done. Demand forecasting is the input for determining the level of reserve, size of the order, ordering cycles, etc. The question that arises is the one of the reliability and accuracy of a forecast and its effects. Forecasting 'by feeling' is not to be dismissed if there is nothing better, but in this case, one must be prepared for forecasting failures that cause unnecessary accumulation of certain spare parts, and also a chronic shortage of other spare parts. All this significantly increases costs and does not provide a satisfactory supply of spare parts. The main problem of the application of this model is that each

  4. Kalman filter estimation model in flood forecasting

    Science.gov (United States)

    Husain, Tahir

    Elementary precipitation and runoff estimation problems associated with hydrologic data collection networks are formulated in conjunction with the Kalman Filter Estimation Model. Examples involve the estimation of runoff using data from a single precipitation station and also from a number of precipitation stations. The formulations demonstrate the role of state-space, measurement, and estimation equations of the Kalman Filter Model in flood forecasting. To facilitate the formulation, the unit hydrograph concept and antecedent precipitation index is adopted in the estimation model. The methodology is then applied to estimate various flood events in the Carnation Creek of British Columbia.

  5. Limited Area Forecasting and Statistical Modelling for Wind Energy Scheduling

    DEFF Research Database (Denmark)

    Rosgaard, Martin Haubjerg

    forecast accuracy for operational wind power scheduling. Numerical weather prediction history and scales of atmospheric motion are summarised, followed by a literature review of limited area wind speed forecasting. Hereafter, the original contribution to research on the topic is outlined. The quality...... control of wind farm data used as forecast reference is described in detail, and a preliminary limited area forecasting study illustrates the aggravation of issues related to numerical orography representation and accurate reference coordinates at ne weather model resolutions. For the o shore and coastal...... sites studied limited area forecasting is found to deteriorate wind speed prediction accuracy, while inland results exhibit a steady forecast performance increase with weather model resolution. Temporal smoothing of wind speed forecasts is shown to improve wind power forecast performance by up to almost...

  6. Evaluation of Coupled Model Forecasts of Ethiopian Highlands Summer Climate

    Directory of Open Access Journals (Sweden)

    Mark R. Jury

    2014-01-01

    Full Text Available This study evaluates seasonal forecasts of rainfall and maximum temperature across the Ethiopian highlands from coupled ensemble models in the period 1981–2006, by comparison with gridded observational products (NMA + GPCC/CRU3. Early season forecasts from the coupled forecast system (CFS are steadier than European community medium range forecast (ECMWF. CFS and ECMWF April forecasts of June–August (JJA rainfall achieve significant fit (r2=0.27, 0.25, resp., but ECMWF forecasts tend to have a narrow range with drought underpredicted. Early season forecasts of JJA maximum temperature are weak in both models; hence ability to predict water resource gains may be better than losses. One aim of seasonal climate forecasting is to ensure that crop yields keep pace with Ethiopia’s growing population. Farmers using prediction technology are better informed to avoid risk in dry years and generate surplus in wet years.

  7. Real-time Social Internet Data to Guide Forecasting Models

    Energy Technology Data Exchange (ETDEWEB)

    Del Valle, Sara Y. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-20

    Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematical approaches and heterogeneous data streams.

  8. Flood forecasting for River Mekong with data-based models

    Science.gov (United States)

    Shahzad, Khurram M.; Plate, Erich J.

    2014-09-01

    In many regions of the world, the task of flood forecasting is made difficult because only a limited database is available for generating a suitable forecast model. This paper demonstrates that in such cases parsimonious data-based hydrological models for flood forecasting can be developed if the special conditions of climate and topography are used to advantage. As an example, the middle reach of River Mekong in South East Asia is considered, where a database of discharges from seven gaging stations on the river and 31 rainfall stations on the subcatchments between gaging stations is available for model calibration. Special conditions existing for River Mekong are identified and used in developing first a network connecting all discharge gages and then models for forecasting discharge increments between gaging stations. Our final forecast model (Model 3) is a linear combination of two structurally different basic models: a model (Model 1) using linear regressions for forecasting discharge increments, and a model (Model 2) using rainfall-runoff models. Although the model based on linear regressions works reasonably well for short times, better results are obtained with rainfall-runoff modeling. However, forecast accuracy of Model 2 is limited by the quality of rainfall forecasts. For best results, both models are combined by taking weighted averages to form Model 3. Model quality is assessed by means of both persistence index PI and standard deviation of forecast error.

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

    Science.gov (United States)

    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.

    2016-10-01

    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.

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

    Science.gov (United States)

    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.

  11. Spatial econometrics: an application to obesity indicators in Argentinian provinces

    OpenAIRE

    Viego, Valentina; Temporelli, Karina

    2010-01-01

    Despite their vigorous expansion in the last 30 years in the field of econometrics, spatial models are still not widely disseminated in the empirical literature in Argentina focused on quantitative aspects of regional scope. This paper aims to present the central lines of spatial econometric analysis applied to the problem of overweight in the population and its determinants. We use provincial data from the National Survey on Risk Factors. The results show that conventional econometric tools ...

  12. Forecasting of Logistics Development Based on Extended Lag Model%基于滞后模型展开的物流发展预测

    Institute of Scientific and Technical Information of China (English)

    郭旭文

    2014-01-01

    In this paper, using the econometric analytic method and with the development of the logistics industry in China as the subject, we forecast the development of the Chinese logistics industry by conducting the necessary ground works, basic statistical analysis, and econometric model establishment.%采用计量经济分析方法作为分析手段,以中国物流业发展作为实证对象,通过基础工作、基础性统计分析、计量模型构建与分析,对中国物流业发展进行了预测研究。

  13. Uncertainty Analysis of Multi-Model Flood Forecasts

    Directory of Open Access Journals (Sweden)

    Erich J. Plate

    2015-12-01

    Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

  14. ECONOMETRICS FOR THE CONNROLLERS

    Directory of Open Access Journals (Sweden)

    Orlov A. I.

    2015-03-01

    Full Text Available Requirements for the professional training of сontrollers include, in particular, the requirements for an intelligent tool that controllers must possess. One of such tools is the econometrics. Organization of training, in particular, preparation of curricula, programs, teaching materials and textbooks, involves discussion of the scope and content of the relevant discipline. We have given the description of the econometric tools of controlling, including the courses of "Econometrics-1" and "Econometrics-2", which the Department of the IBM-2 "Economics and organization of production" is on the faculty "Engineering and Business Management" of Bauman Moscow State Technical University. We have discussed the external environment of econometrics and the necessary changes in it. For example, the course of "Probability Theory and Mathematical Statistics" is the basis for the study of econometrics. However, it has to be brought into line with modern requirements. In particular, it is necessary to consider such things as random elements with values in an arbitrary space, empirical and theoretical means in such spaces, to prove the laws of large numbers in general statements. Simultaneously with the specified extension course content is reasonable to exclude from the program methods based on those assumptions are not met in the concrete economic situations. In particular, we have to eliminate the one-sample and two-sample Student's t tests and replace them with the corresponding nonparametric tests. We do not need the "classical" and geometric probability, etc. We have given the importance of the problem of constructing integral indicators in various problems of econometrics; issues of analysis of the situation by means of a system of indicators are discussed in detail

  15. Constrained regression models for optimization and forecasting

    Directory of Open Access Journals (Sweden)

    P.J.S. Bruwer

    2003-12-01

    Full Text Available Linear regression models and the interpretation of such models are investigated. In practice problems often arise with the interpretation and use of a given regression model in spite of the fact that researchers may be quite "satisfied" with the model. In this article methods are proposed which overcome these problems. This is achieved by constructing a model where the "area of experience" of the researcher is taken into account. This area of experience is represented as a convex hull of available data points. With the aid of a linear programming model it is shown how conclusions can be formed in a practical way regarding aspects such as optimal levels of decision variables and forecasting.

  16. A CONTINUING APPROACH, FROM FINANCIAL ECONOMICS TO FINANCIAL ECONOMETRICS OR THE ECONOMETRIC THINKING APPLIED TO FINANCIAL ECONOMICS

    OpenAIRE

    Gheorghe Săvoiu; Constantin Manea

    2013-01-01

    The main aim of this paper is to attempt a theoretical delineation of a new econoscience now known as financial econometrics, which is as a result of a dual approach, one originally from economics to econometrics, followed by another one, articulate, from financial economics to financial econometrics, both purely theoretical, simultaneously stressing the importance of economic and financial modelling, historically detailing the emergence and development of this new econoscience, outlining its...

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Xianlei Dong

    Full Text Available 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.

  19. A Problem Study on Econometric Model Diversity Paradox%“计量模型多样性悖论”研究

    Institute of Scientific and Technical Information of China (English)

    俞立平; 刘骏

    2016-01-01

    在排除模型误用以后,将经济学研究中针对同一问题的研究,不同计量模型的结果各不相同,甚至结论截然相反问题称为“计量模型多样性悖论”。分析此问题产生的原因,主要是关键变量的多样性、方程形式设定的多样性和计量模型的多样性,提出采用元分析来解决这个问题,指出在元分析应用过程中应力求单个模型的精确,尽可能多地采用合适的模型,注意同类性质的结果才能进行元分析。同时,讨论元分析带来的新问题,比如对研究团队的计量经济学水平要求较高,增加了成本,延长了研究的时间,也加大了论文的篇幅等,但这是计量经济学发展过程中的正常现象,并不涉及采用元分析解决“计量模型多样性悖论”问题的科学性。%This paper will be on the same problems in the research of economics study ,the results of different econometric model each are not identical ,even conclusion is in contrast to the problem is known as the paradox of econometric model diversity after the exclusion of econometric model misuse .It analyzes the main causes of this problem is the diversity of key variables ,forms of equation and econometric model , and proposes to use meta analysis to solve this problem .It notes that we should seek a single accurate model ,as much as possible using a suitable model ,pay attention to the similar nature results in the meta‐analysis application process .The authors also discuss the new problems caused by meta analysis ,such as higher econometric level requirements for the research team ,increased costs ,extend the time to study , and also increased the length of the paper ,etc .But the authors think this is the normal phenomenon in the process of econometrics development and it does not involve the use of meta analysis to solve the"econometric model diversity paradox"problem scientifically .

  20. PETRA. The Forecast Model. Synthesis report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-09-01

    The aim of the PETRA project was to develop a model that could recreate the main aspects involved in the demand for travel. The attainment of this objective requires that the model system should retain a high degree of detail and be based on disaggregate models. This was both to ensure an accurate representation of the underlying behavioural intentions, and allow analysis of the underlying travel demand and related aspects across a number of dimensions. This has been achieved in all main respects. The model system is capable of close reproduction of the observed behaviour and generally responds as expected to changes, exhibiting consistent and plausible reactions. The dis-aggregation of the forecast population, according to the various criteria, allows the model to clearly illustrates the behavioural differences between different population segments. Thus, it seems reasonable to conclude that PETRA is capable of detailed analyses of the distributional and behavioural effects of policy changes. (au) EFP-94. 20 refs.

  1. Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations

    Directory of Open Access Journals (Sweden)

    Brdyś Mietek A.

    2016-03-01

    Full Text Available The paper considers the forecasting of the euro/Polish złoty (EUR/PLN spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day-ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space wavelet network model is, in contrast to econometric forecast combinations, a non-parametric prediction technique which does not make any distributional assumptions regarding the underlying input variables. Both methods can be used as forecasting tools in portfolio investment management, asset valuation, IT security and integrated business risk intelligence in volatile market conditions.

  2. Guidance on the Choice of Threshold for Binary Forecast Modeling

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This paper proposes useful guidance on the choice of threshold for binary forecasts. In weather forecast systems, the probabilistic forecast cannot be used directly when estimated too smoothly. In this case, the binary forecast, whether a meteorological event will occur or not, is preferable to the probabilistic forecast.A threshold is needed to generate a binary forecast, and the guidance in this paper encompasses the use of skill scores for the choice of threshold according to the forecast pattern. The forecast pattern consists of distribution modes of estimated probabilities, occurrence rates of observations, and variation modes.This study is performed via Monte-Carlo simulation, with 48 forecast patterns considered. Estimated probabilities are generated by random variate sampling from five distributions separately. Varying the threshold from 0 to 1, binary forecasts are generated by threshold. For the assessment of binary forecast models, a 2×2 contingency table is used and four skill scores (Heidke skill score, hit rate, true skill statistic,and threat score) are compared for each forecast pattern. As a result, guidance on the choice of skill score to find the optimal threshold is proposed.

  3. PV power forecast using a nonparametric PV model

    OpenAIRE

    Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis

    2015-01-01

    Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quant...

  4. FORECASTING ELECTRICITY PRICES IN DEREGULATED WHOLESALE SPOT ELECTRICITY MARKET - A REVIEW

    Directory of Open Access Journals (Sweden)

    Girish Godekere Panchakshara Murthy,

    2014-01-01

    Full Text Available In the new framework of competitive electricity markets, all power market participants need accurate price forecasting tools. Electricity price forecasts characterize significant information that can help captive power producer, independent power producer, power generation companies, power distribution companies or open access consumers in careful planning of their bidding strategies for maximizing their profits, benefits and utilities from long term, medium term and short term perspective. Short term spot electricity price forecasting techniques are either inspired from electrical engineering literature (i.e. load forecasting or from economics literature (i.e. game theory models and the time-series econometric models. In this study we investigate the emergence of spot electricity markets with particular emphasis on Indian electricity market which has never been done before and review selected finance and econometrics inspired literature and models for forecasting electricity spot prices in deregulated wholesale spot electricity markets.

  5. Grey forecasting model for active vibration control systems

    Science.gov (United States)

    Lihua, Zou; Suliang, Dai; Butterworth, John; Ma, Xing; Dong, Bo; Liu, Aiping

    2009-05-01

    Based on the grey theory, a GM(1,1) forecasting model and an optimal GM(1,1) forecasting model are developed and assessed for use in active vibration control systems for earthquake response mitigation. After deriving equations for forecasting the control state vector, design procedures for an optimal active control method are proposed. Features of the resulting vibration control and the influence on it of time-delay based on different sampling intervals of seismic ground motion are analysed. The numerical results show that the forecasting models based on the grey theory are reliable and practical in structural vibration control fields. Compared with the grey forecasting model, the optimal forecasting model is more efficient in reducing the influences of time-delay and disturbance errors.

  6. Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K.

    OpenAIRE

    Promponas, Pantelis; Peel, David Alan

    2016-01-01

    Exchange rate forecasting has become an arena for many researchers the last decades while predictability depends heavily on several factors such as the choice of the fundamentals, the econometric model and the data form. The aim of this paper is to assess whether modelling time-variation and other forms of instabilities may improve the forecasting performance of the models. Paper begins with a brief critical review of the recently developed exchange rate forecasting models and continues with ...

  7. Modeling olive-crop forecasting in Tunisia

    Science.gov (United States)

    Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji

    2016-01-01

    Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.

  8. Forecast of future aviation fuels: The model

    Science.gov (United States)

    Ayati, M. B.; Liu, C. Y.; English, J. M.

    1981-01-01

    A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

  9. Analysis of Integrated Econometric and Input-Output Model%投入产出与计量经济联合模型研究

    Institute of Scientific and Technical Information of China (English)

    孟彦菊; 向蓉美

    2011-01-01

    经典投入产出(Input-output,IO)模型是一个线性性和确定性系统.尽管IO模型对现实经济世界的描述只是一种近似,但它所特有的细致的部门分类,能深刻揭示某一时点国民经济各部门之间的数量依存关系.计量经济(Econometric,EC)模型具有动态性优点,它能通过概率论来处理现实世界的不确定性.本文试图结合这两种模型的优点,尝试着建立EC+IO联合模型,并运用中国数据进行实证分析,结果证明联合模型能够更真实地模拟宏观经济发展,进行更准确的预测.%The classical input-output (IO) model is a popular linear and deterministic system. Although it can only approximately describe the real-world economy, IO model can reveal the dependency among different economic sectors at a particular point of time through static or cross-sectional model. On the other hand, the econometric (EC) model is a dynamic system and can deal with the uncertainty in the real economy by means of probability theory. This paper tries to integrate econometric (EC) model and input-output (IO) model to combine their advantages. An empirical study with china data was conducted and it is shown that the integrated model can simulate the macroeconomic more realistically and thus make prediction more accurately.

  10. Neural network versus classical time series forecasting models

    Science.gov (United States)

    Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

    2017-05-01

    Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Razana Alwee

    2013-01-01

    Full Text Available 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.

  13. A Stochastic-Dynamic Model for Real Time Flood Forecasting

    Science.gov (United States)

    Chow, K. C. A.; Watt, W. E.; Watts, D. G.

    1983-06-01

    A stochastic-dynamic model for real time flood forecasting was developed using Box-Jenkins modelling techniques. The purpose of the forecasting system is to forecast flood levels of the Saint John River at Fredericton, New Brunswick. The model consists of two submodels: an upstream model used to forecast the headpond level at the Mactaquac Dam and a downstream model to forecast the water level at Fredericton. Inputs to the system are recorded values of the water level at East Florenceville, the headpond level and gate position at Mactaquac, and the water level at Fredericton. The model was calibrated for the spring floods of 1973, 1974, 1977, and 1978, and its usefulness was verified for the 1979 flood. The forecasting results indicated that the stochastic-dynamic model produces reasonably accurate forecasts for lead times up to two days. These forecasts were then compared to those from the existing forecasting system and were found to be as reliable as those from the existing system.

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

    Science.gov (United States)

    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.

  15. Development of Ensemble Model Based Water Demand Forecasting Model

    Science.gov (United States)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  16. Comment on "Polynomial cointegration tests of anthropogenic impact on global warming" by Beenstock et al. (2012) - some hazards in econometric modelling of climate change

    Science.gov (United States)

    Pretis, F.; Hendry, D. F.

    2013-10-01

    We outline six important hazards that can be encountered in econometric modelling of time-series data, and apply that analysis to demonstrate errors in the empirical modelling of climate data in Beenstock et al. (2012). We show that the claim made in Beenstock et al. (2012) as to the different degrees of integrability of CO2 and temperature is incorrect. In particular, the level of integration is not constant and not intrinsic to the process. Further, we illustrate that the measure of anthropogenic forcing in Beenstock et al. (2012), a constructed "anthropogenic anomaly", is not appropriate regardless of the time-series properties of the data.

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

    Science.gov (United States)

    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.

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

    CERN Document Server

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

    2016-01-01

    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.

  19. Compilation Of An Econometric Human Resource Efficiency Model For Project Management Best Practices

    OpenAIRE

    Van Zyl, G.; P Venier

    2006-01-01

    The aim of the paper is to introduce a human resource efficiency model in order to rank the most important human resource driving forces for project management best practices. The results of the model will demonstrate how the human resource component of project management acts as the primary function to enhance organizational performance, codified through improved logical end-state programmes, work ethics and process contributions. Given the hypothesis that project management best practices i...

  20. Forecasting natural gas consumption in China by Bayesian Model Averaging

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2015-11-01

    Full Text Available With rapid growth of natural gas consumption in China, it is in urgent need of more accurate and reliable models to make a reasonable forecast. Considering the limitations of the single model and the model uncertainty, this paper presents a combinative method to forecast natural gas consumption by Bayesian Model Averaging (BMA. It can effectively handle the uncertainty associated with model structure and parameters, and thus improves the forecasting accuracy. This paper chooses six variables for forecasting the natural gas consumption, including GDP, urban population, energy consumption structure, industrial structure, energy efficiency and exports of goods and services. The results show that comparing to Gray prediction model, Linear regression model and Artificial neural networks, the BMA method provides a flexible tool to forecast natural gas consumption that will have a rapid growth in the future. This study can provide insightful information on natural gas consumption in the future.

  1. A MODEL FOR THE PALM OIL MARKET IN NIGERIA: AN ECONOMETRICS APPROACH

    Directory of Open Access Journals (Sweden)

    Henry Egwuma

    2016-04-01

    Full Text Available The aim of this study is to formulate and estimate a model for the palm oil market in Nigeria with a view to identifying principal factors that shape the Nigerian palm oil industry. Four structural equation models comprising palm oil production, import demand, domestic demand and producer price have been estimated using the autoregressive distributed lag (ARDL cointegration approach over the 1970 to 2011 period. The results reveal that significant factors that influence the Nigerian palm oil industry include the own price, technological improvements, and income level. Government expenditure on agricultural development is also an important determinant, which underscores the need for government support in agriculture. Our model provides a useful framework for analyzing the effects of changes in major exogenous variables such as income or import tariff on the production, demand, and price of palm oil.

  2. Compilation Of An Econometric Human Resource Efficiency Model For Project Management Best Practices

    Directory of Open Access Journals (Sweden)

    G. van Zyl

    2006-11-01

    Full Text Available The aim of the paper is to introduce a human resource efficiency model in order to rank the most important human resource driving forces for project management best practices. The results of the model will demonstrate how the human resource component of project management acts as the primary function to enhance organizational performance, codified through improved logical end-state programmes, work ethics and process contributions. Given the hypothesis that project management best practices involve significant human resource and organizational changes, one would reasonably expect this process to influence and resonate throughout all the dimensions of an organisation.

  3. A Simple Hybrid Model for Short-Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Suseelatha Annamareddi

    2013-01-01

    Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  6. SARS epidemical forecast research in mathematical model

    Institute of Scientific and Technical Information of China (English)

    DING Guanghong; LIU Chang; GONG Jianqiu; WANG Ling; CHENG Ke; ZHANG Di

    2004-01-01

    The SIJR model, simplified from the SEIJR model, is adopted to analyze the important parameters of the model of SARS epidemic such as the transmission rate, basic reproductive number. And some important parameters are obtained such as the transmission rate by applying this model to analyzing the situation in Hong Kong, Singapore and Canada at the outbreak of SARS. Then forecast of the transmission of SARS is drawn out here by the adjustment of parameters (such as quarantined rate) in the model. It is obvious that inflexion lies on the crunode of the graph, which indicates the big difference in transmission characteristics between the epidemic under control and not under control. This model can also be used in the comparison of the control effectiveness among different regions. The results from this model match well with the actual data in Hong Kong, Singapore and Canada and as a by-product, the index of the effectiveness of control in the later period can be acquired. It offers some quantitative indexes, which may help the further research in epidemic diseases.

  7. Factor Model Forecasting of Inflation in Croatia

    Directory of Open Access Journals (Sweden)

    Davor Kunovac

    2007-12-01

    Full Text Available This paper tests whether information derived from 144 economic variables (represented by only a few constructed factors can be used for the forecasting of consumer prices in Croatia. The results obtained show that the use of one factor enhances the precision of the benchmark model’s ability to forecast inflation. The methodology used is sufficiently general to be able to be applied directly for the forecasting of other economic variables.

  8. Factor Model Forecasts of Exchange Rates

    OpenAIRE

    Charles Engel; Nelson C. Mark; Kenneth D. West

    2012-01-01

    We construct factors from a cross section of exchange rates and use the idiosyncratic deviations from the factors to forecast. In a stylized data generating process, we show that such forecasts can be effective even if there is essentially no serial correlation in the univariate exchange rate processes. We apply the technique to a panel of bilateral U.S. dollar rates against 17 OECD countries. We forecast using factors, and using factors combined with any of fundamentals suggested by Taylor r...

  9. Housing land transaction data and structural econometric estimation of preference parameters for urban economic simulation models.

    Science.gov (United States)

    Caruso, Geoffrey; Cavailhès, Jean; Peeters, Dominique; Thomas, Isabelle; Frankhauser, Pierre; Vuidel, Gilles

    2015-12-01

    This paper describes a dataset of 6284 land transactions prices and plot surfaces in 3 medium-sized cities in France (Besançon, Dijon and Brest). The dataset includes road accessibility as obtained from a minimization algorithm, and the amount of green space available to households in the neighborhood of the transactions, as evaluated from a land cover dataset. Further to the data presentation, the paper describes how these variables can be used to estimate the non-observable parameters of a residential choice function explicitly derived from a microeconomic model. The estimates are used by Caruso et al. (2015) to run a calibrated microeconomic urban growth simulation model where households are assumed to trade-off accessibility and local green space amenities.

  10. RESULTS OF INTERBANK EXCHANGE RATES FORECASTING USING STATE SPACE MODEL

    Directory of Open Access Journals (Sweden)

    Muhammad Kashif

    2008-07-01

    Full Text Available This study evaluates the performance of three alternative models for forecasting daily interbank exchange rate of U.S. dollar measured in Pak rupees. The simple ARIMA models and complex models such as GARCH-type models and a state space model are discussed and compared. Four different measures are used to evaluate the forecasting accuracy. The main result is the state space model provides the best performance among all the models.

  11. Identifiability and Problems of Model Selection for Time-Series Analysis in Econometrics.

    Science.gov (United States)

    1980-01-01

    feedback", in Proc. 1971 NRL-.IS Con- ference on Ordinary Differential Equations , edited by L. Weiss, Acalumic Press, pazes 459-471. REKA Dr Pai-,e...abstract sense. The difficulty is nonuniqueness , not identifiability. Third, there is the question of parametrization of models. In econo- metrics...with the equation (a) + -1, O, i, where u i t is a linear stochastic process whose values ( h ) " f K c t _ Tr=O ’t - are generated with the aid of

  12. Shemya, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Shemya, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  13. Palm Beach, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Palm Beach, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  14. Haleiwa, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Haleiwa, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  15. Key West, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Key West, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  16. Sitka, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Sitka, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  17. Monterey, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Monterey, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  18. Ponce, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ponce, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  19. Port Alexander, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Alexander, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  20. Port Orford, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Orford, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  1. Seward, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Seward, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  2. Nawiliwili, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nawiliwili, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  3. Montauk, New York Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Montauk, New York Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  4. San Juan, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The San Juan, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  5. Arecibo, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Arecibo, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  6. Toke Point, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Toke Point, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  7. Hilo, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hilo, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  8. Ocean City, Maryland Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ocean City, Maryland Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  9. Keauhou, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Keauhou, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  10. Honolulu, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Honolulu, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  11. San Diego, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The San Diego, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  12. Adak, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Adak, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  13. Garibaldi, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Garibaldi, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  14. Kihei, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kihei, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  15. Kahului, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kahului, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  16. Daytona Beach, Florida Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Daytona Beach, Florida Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  17. Mayaguez, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Mayaguez, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  18. Savannah, Georgia Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Savannah, Georgia Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  19. Homer, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Homer, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  20. King Cove, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The King Cove, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  1. Portland, Maine Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Portland, Maine Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  2. La Push, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The La Push, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  3. Seaside, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Seaside, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  4. Fajardo, Puerto Rico Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Fajardo, Puerto Rico Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  5. Kawaihae, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kawaihae, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  6. Nikolski, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nikolski, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  7. Kodiak, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kodiak, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is...

  8. Sand Point, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Sand Point, Alaska Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  9. Pearl Harbor, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Pearl Harbor, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  10. Florence, Oregon Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Florence, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  11. Hanalei, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hanalei, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  12. Lahaina, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Lahaina, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  13. Wake Island Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Wake Island Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  14. Kailua-Kona, Hawaii Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Kailua-Kona, Hawaii Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  15. Apra Harbor, Guam Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Apra Harbor, Guam Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...

  16. Westport, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Westport, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  17. Neah Bay, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Neah Bay, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model....

  18. When mechanism matters: Bayesian forecasting using models of ecological diffusion

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

    2017-01-01

    Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

  19. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    Science.gov (United States)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  20. Planetary Kp index forecast using autoregressive models

    CERN Document Server

    Gonzalez, Arian Ojeda; Odriozola, Siomel Savio; Rosa, Reinaldo Roberto; Mendes, Odim

    2014-01-01

    The geomagnetic Kp index is derived from the K index measurements obtained from thirteen stations located around the Earth geomagnetic latitudes between $48^\\circ$ and $63^\\circ$. This index is processed every three hours, is quasi-logarithmic and estimates the geomagnetic activity. The Kp values fall within a range of 0 to 9 and are organized as a set of 28 discrete values. The data set is important because it is used as one of the many input parameters of magnetospheric and ionospheric models. The objective of this work is to use historical data from the Kp index to develop a methodology to make a prediction in a time interval of at least three hours. Five different models to forecast geomagnetic indices Kp and ap are tested. Time series of values of Kp index from 1932 to 15/12/2012 at 21:00 UT are used as input to the models. The purpose of the model is to predict the three measured values after the last measured value of the Kp index (it means the next 9 hours values). The AR model provides the lowest com...

  1. Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling.

    Science.gov (United States)

    Terza, Joseph V; Basu, Anirban; Rathouz, Paul J

    2008-05-01

    The paper focuses on two estimation methods that have been widely used to address endogeneity in empirical research in health economics and health services research-two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI). 2SPS is the rote extension (to nonlinear models) of the popular linear two-stage least squares estimator. The 2SRI estimator is similar except that in the second-stage regression, the endogenous variables are not replaced by first-stage predictors. Instead, first-stage residuals are included as additional regressors. In a generic parametric framework, we show that 2SRI is consistent and 2SPS is not. Results from a simulation study and an illustrative example also recommend against 2SPS and favor 2SRI. Our findings are important given that there are many prominent examples of the application of inconsistent 2SPS in the recent literature. This study can be used as a guide by future researchers in health economics who are confronted with endogeneity in their empirical work.

  2. Econometric modelling of risk adverse behaviours of entrepreneurs in the provision of house fittings in China

    Directory of Open Access Journals (Sweden)

    Rita Yi Man Li

    2012-03-01

    Full Text Available Entrepreneurs have always born the risk of running their business. They reap a profit in return for their risk taking and work. Housing developers are no different. In many countries, such as Australia, the United Kingdom and the United States, they interpret the tastes of the buyers and provide the dwellings they develop with basic fittings such as floor and wall coverings, bathroom fittings and kitchen cupboards. In mainland China, however, in most of the developments, units or houses are sold without floor or wall coverings, kitchen  or bathroom fittings. What is the motive behind this choice? This paper analyses the factors affecting housing developers’ decisions to provide fittings based on 1701 housing developments in Hangzhou, Chongqing and Hangzhou using a Probit model. The results show that developers build a higher proportion of bare units in mainland China when: 1 there is shortage of housing; 2 land costs are high so that the comparative costs of providing fittings become relatively low.

  3. A review of operational, regional-scale, chemical weather forecasting models in Europe

    NARCIS (Netherlands)

    Kukkonen, J.; Olsson, T.; Schultz, D.M.; Baklanov, A.; Klein, T.; Miranda, A.I.; Monteiro, A.; Hirtl, M.; Tarvainen, V.; Boy, M.; Peuch, V.-H.; Poupkou, A.; Kioutsioukis, I.; Finardi, S.; Sofiev, M.; Sokhi, R.; Lehtinen, K.E.J.; Karatzas, K.; San José, R.; Astitha, M.; Kallos, G.; Schaap, M.; Reimer, E.; Jakobs, H.; Eben, K.

    2012-01-01

    Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed

  4. Forecasting Financial Time Series Using Model Averaging

    NARCIS (Netherlands)

    F. Ravazzolo (Francesco)

    2007-01-01

    textabstractIn almost all cases a decision maker cannot identify ex ante the true process. This observation has led researchers to introduce several sources of uncertainty in forecasting exercises. In this context, the research reported in these pages finds an increase of forecasting power o

  5. A REVIEW OF ECONOMETRIC ESTIMATION OF CONSUMER DEMAND FOR AUTOMOBILES AND THE COUNTRY OF ORIGIN (COO EFFECTS

    Directory of Open Access Journals (Sweden)

    AHMET ÖZÇAM

    2013-06-01

    Full Text Available Abstract.  The demand for automobiles has always been an important area for the application of various theoretical econometric models and the automotive industry remains to be the leading export sector in Turkey. This paper surveys a range of important developments in the modeling and estimation of demand for new automobiles in various regions like the U.S., Europe, and Turkey. The applied econometrician can acquire a good perspective when constructing such a model and deciding on the most appropriate econometric estimation technique. However, the applied econometrician is cautioned against some of the difficulties in modeling and forecasting the demand for automobiles. Since the Country of Origin (COO effect is known to be an important factor in explaining the demand for products including automobiles, we also review the marketing literature investigating the consumers’ biases about products related to the country in which they are made.

  6. Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

    Science.gov (United States)

    O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

    2015-04-01

    Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

  7. Forecasting project schedule performance using probabilistic and deterministic models

    Directory of Open Access Journals (Sweden)

    S.A. Abdel Azeem

    2014-04-01

    Full Text Available Earned value management (EVM was originally developed for cost management and has not widely been used for forecasting project duration. In addition, EVM based formulas for cost or schedule forecasting are still deterministic and do not provide any information about the range of possible outcomes and the probability of meeting the project objectives. The objective of this paper is to develop three models to forecast the estimated duration at completion. Two of these models are deterministic; earned value (EV and earned schedule (ES models. The third model is a probabilistic model and developed based on Kalman filter algorithm and earned schedule management. Hence, the accuracies of the EV, ES and Kalman Filter Forecasting Model (KFFM through the different project periods will be assessed and compared with the other forecasting methods such as the Critical Path Method (CPM, which makes the time forecast at activity level by revising the actual reporting data for each activity at a certain data date. A case study project is used to validate the results of the three models. Hence, the best model is selected based on the lowest average percentage of error. The results showed that the KFFM developed in this study provides probabilistic prediction bounds of project duration at completion and can be applied through the different project periods with smaller errors than those observed in EV and ES forecasting models.

  8. Study on Population Forecast Model in Planning of Land Use

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    On the basis of describing characteristics and condition of application of natural growth model of population,weighted average growth model,regression forecast model and GM(1,1) forecast model,taking Gushi County in Henan Province as an example,according to the statistics of population in Gushi County Statistical Yearbook from 1991 to 2007,we establish four models to conduct fitting on population change respectively,and meanwhile,we predict population size from 2008 to 2009 and conduct preciseness test on the population size.The test results show that the preciseness of forecast results of natural growth model is not high,and the preciseness of forecast results of weighted average growth model is not scientific when the total size of population is unstable.The results of GM(1,1) forecast model and regression forecast model largely conform to the actual data,so we can take the mean of the two as the final forecast result.

  9. Operational forecasting based on a modified Weather Research and Forecasting model

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  10. The Status of Bridge Principles in Applied Econometrics

    Directory of Open Access Journals (Sweden)

    Bernt P. Stigum

    2016-12-01

    Full Text Available The paper begins with a figurative representation of the contrast between present-day and formal applied econometrics. An explication of the status of bridge principles in applied econometrics follows. To illustrate the concepts used in the explication, the paper presents a simultaneous-equation model of the equilibrium configurations of a perfectly competitive commodity market. With artificially generated data I carry out two empirical analyses of such a market that contrast the prescriptions of formal econometrics in the tradition of Ragnar Frisch with the commands of present-day econometrics in the tradition of Trygve Haavelmo. At the end I demonstrate that the bridge principles I use in the formal-econometric analysis are valid in the Real World—that is in the world in which my data reside.

  11. Testing probabilistic adaptive real-time flood forecasting models

    NARCIS (Netherlands)

    Smith, P.J.; Beven, K.J.; Leedal, D.; Weerts, A.H.; Young, P.C.

    2014-01-01

    Operational flood forecasting has become a complex and multifaceted task, increasingly being treated in probabilistic ways to allow for the inherent uncertainties in the forecasting process. This paper reviews recent applications of data-based mechanistic (DBM) models within the operational UK Natio

  12. Combined forecasts from linear and nonlinear time series models

    NARCIS (Netherlands)

    N. Terui (Nobuhiko); H.K. van Dijk (Herman)

    1999-01-01

    textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally (non)line

  13. Forecasts of time averages with a numerical weather prediction model

    Science.gov (United States)

    Roads, J. O.

    1986-01-01

    Forecasts of time averages of 1-10 days in duration by an operational numerical weather prediction model are documented for the global 500 mb height field in spectral space. Error growth in very idealized models is described in order to anticipate various features of these forecasts and in order to anticipate what the results might be if forecasts longer than 10 days were carried out by present day numerical weather prediction models. The data set for this study is described, and the equilibrium spectra and error spectra are documented; then, the total error is documented. It is shown how forecasts can immediately be improved by removing the systematic error, by using statistical filters, and by ignoring forecasts beyond about a week. Temporal variations in the error field are also documented.

  14. Multilayer stock forecasting model using fuzzy time series.

    Science.gov (United States)

    Javedani Sadaei, Hossein; Lee, Muhammad Hisyam

    2014-01-01

    After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.

  15. Modeling and Forecasting Volatility of the Malaysian Stock Markets

    Directory of Open Access Journals (Sweden)

    Ahmed Shamiri

    2009-01-01

    Full Text Available Problem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian. Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments of the distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily on the choice of error distribution than the choice of GARCH models.

  16. Port San Luis, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port San Luis, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  17. Charlotte Amalie, Virgin Islands Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Charlotte Amalie, Virgin Islands Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami...

  18. Pago Pago, American Samoa Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Pago Pago, American Samoa Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  19. Point Reyes, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Point Reyes, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  20. Morehead City, North Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Morehead City, North Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  1. Cordova, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Cordova, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  2. Craig, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Craig, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  3. Virginia Beach Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Virginia Beach, Virginia Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  4. Unalaska, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Unalaska, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  5. A simulation model for forecasting downhill ski participation

    Science.gov (United States)

    Daniel J. Stynes; Daniel M. Spotts

    1980-01-01

    The purpose of this paper is to describe progress in the development of a general computer simulation model to forecast future levels of outdoor recreation participation. The model is applied and tested for downhill skiing in Michigan.

  6. Santa Barbara, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Santa Barbara, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  7. Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Elfin Cove, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  8. Los Angeles, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Los Angeles, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  9. British Columbia, Canada Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The British Columbia, Canada Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  10. Cape Hatteras, North Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Cape Hatteras, North Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  11. Myrtle Beach, South Carolina Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Myrtle Beach, South Carolina Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  12. San Francisco, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The San Francisco, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  13. Santa Monica, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Santa Monica, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  14. Atlantic City, New Jersey Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Atlantic City, New Jersey Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  15. Atka, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Atka, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite...

  16. Nantucket, Massachusetts Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nantucket, Massachusetts Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  17. Crescent City, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Crescent City, California Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  18. Chignik, Alaska Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Chignik, Alaska Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  19. Port Angeles, Washington Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Port Angeles, Washington Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  20. Christiansted, Virgin Islands Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Christiansted, Virgin Islands Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST)...

  1. Eureka, California Tsunami Forecast Grids for MOST Model

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Eureka, California Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a...

  2. Asymptotic distribution of Moran test in spatial econometric autoregressive models%空间经济计量滞后模型Moran检验的渐近分布

    Institute of Scientific and Technical Information of China (English)

    欧变玲; 龙志和; 林光平

    2011-01-01

    基于空间经济计量滞后模型的2SLS残差,证明误差项服从正态独立同分布时,空间滞后模型Moran检验渐近服从正态分布,提出OLL-Moran检验②.Monte Carlo实验结果显示,与KP-Moran检验相比,提出的OLL-Moran检验的水平扭曲更低、功效更高.OLL-Moran检验具有良好的有限样本性质,能够更有效地检验空间经济计量滞后模型估计残差间的空间关系.%In this paper, based on the 2SLS residuals in the spatial econometric autoregressive model, we prove that Moran test is asymptotically normal distribution when the error is independent and identically distributed , and then establish OLL-Moran test. Monte Carlo experiment results show that size distortion of OLL-Moran test in this research is less than that of KP-Moran, and the power of OLL-Moran test is more than that of KP-Moran. OLL-Moran test has good finite sample performance, and could check effectively spatial correlation among 2SLS residuals in the spatial econometric autoregressive model.

  3. Sea Fog Forecasting with Lagrangian Models

    Science.gov (United States)

    Lewis, J. M.

    2014-12-01

    In 1913, G. I. Taylor introduced us to a Lagrangian view of sea fog formation. He conducted his study off the coast of Newfoundland in the aftermath of the Titanic disaster. We briefly review Taylor's classic work and then apply these same principles to a case of sea fog formation and dissipation off the coast of California. The resources used in this study consist of: 1) land-based surface and upper-air observations, 2) NDBC (National Data Buoy Center) observations from moored buoys equipped to measure dew point temperature as well as the standard surface observations at sea (wind, sea surface temperature, pressure, and air temperature), 3) satellite observations of cloud, and 4) a one-dimensional (vertically directed) boundary layer model that tracks with the surface air motion and makes use of sophisticated turbulence-radiation parameterizations. Results of the investigation indicate that delicate interplay and interaction between the radiation and turbulence processes makes accurate forecasts of sea fog onset unlikely in the near future. This pessimistic attitude stems from inadequacy of the existing network of observations and uncertainties in modeling dynamical processes within the boundary layer.

  4. International oil and natural gas demand projections: an econometric model for 2008-2030; Projecao das demandas mundiais de petroleo e de gas natural: aplicacao de um modelo agregado para o periodo 2008-2030

    Energy Technology Data Exchange (ETDEWEB)

    Machado, Giovani; Aragao, Amanda; Valle, Ricardo Nascimento e Silva do [Empresa de Pesquisa Energetica (EPE), Rio de Janeiro, RJ (Brazil)

    2008-07-01

    This study forecasts the world oil and gas demands for 2008-2030 by applying econometric formulations. The basic variables are world GDP and Brent price. The forecast assumptions are: sound world economic growth remains, despite falling rates during the period; Brent prices continue high, but in a lower level, in 2006 constant prices, in harmony with Energy Information Administration reference scenario. Findings show that, should assumptions prove to be correct, world oil and gas demands will reach 118 million bbl/d and 5 trillion cubic meters in 2030, respectively. In other words, world oil demand will grow at 1.4% per year, while world gas demand will increase at 2.5% per year. Although such figures are similar to those from other institutions (EIA, IEA and OPEC), structural changes in oil and gas markets, catalyzed by high oil prices and energy and environmental policies, may reduce forecast strength of the specifications proposed. (author)

  5. Evaluation of statistical models for forecast errors from the HBV model

    Science.gov (United States)

    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.

  6. Decision Making Models Using Weather Forecast Information

    OpenAIRE

    Hiramatsu, Akio; Huynh, Van-Nam; Nakamori, Yoshiteru

    2007-01-01

    The quality of weather forecast has gradually improved, but weather information such as precipitation forecast is still uncertainty. Meteorologists have studied the use and economic value of weather information, and users have to translate weather information into their most desirable action. To maximize the economic value of users, the decision maker should select the optimum course of action for his company or project, based on an appropriate decision strategy under uncertain situations. In...

  7. Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Marin Cerjan

    2014-05-01

    Full Text Available Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

  8. Modeling and Forecasting the Volatility of Eastern European Emerging Markets

    Directory of Open Access Journals (Sweden)

    Sang Hoon Kang

    2009-06-01

    Full Text Available This study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, naThis study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, namely, Hungary, Poland, Russia, and Slovakia. From the results of our empirical analysis, we found that the FIGARCH model is better equipped to capture the long memory property in the volatility of these markets than the GARCH and IGARCH models. More importantly, the FIGARCH model is found to provide superior performance in one-day-ahead volatility forecasts. Thus, this study recommends researchers, portfolio managers, and traders to use the long memory FIGARCH model in analyzing and forecasting the volatility dynamics of Eastern European emerging markets.

  9. Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2015-01-01

    We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...

  10. Verification of short lead time forecast models: applied to Kp and Dst forecasting

    Science.gov (United States)

    Wintoft, Peter; Wik, Magnus

    2016-04-01

    In the ongoing EU/H2020 project PROGRESS models that predicts Kp, Dst, and AE from L1 solar wind data will be used as inputs to radiation belt models. The possible lead times from L1 measurements are shorter (10s of minutes to hours) than the typical duration of the physical phenomena that should be forecast. Under these circumstances several metrics fail to single out trivial cases, such as persistence. In this work we explore metrics and approaches for short lead time forecasts. We apply these to current Kp and Dst forecast models. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 637302.

  11. Improving of local ozone forecasting by integrated models.

    Science.gov (United States)

    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.

  12. TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS: SELECTING OR COMBINING?

    Institute of Scientific and Technical Information of China (English)

    YU Lean; WANG Shouyang; K. K. Lai; Y.Nakamori

    2005-01-01

    Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.

  13. A Modeler's Perspective on Space Weather Forecasting (Invited)

    Science.gov (United States)

    Wiltberger, M. J.

    2010-12-01

    Space physics is moving into a new era where numerical models originally developed for answering science questions are used as the basis for making operational space weather forecasts. Answering this challenge requires developments on multiple fronts requiring collaborations across space physics disciplines and between the research and operations communities. Since space weather in geospace is driven by the solar wind conditions a natural solution to improving the forecast lead time is to couple geospace models to heliospheric models. The quality of these forecast is dependent upon the ability of the heliospheric models to accurately model IMF Bz. Another challenge presented by moving into the forecasting arena is preparing the models for real-time operation which includes both computational performance and data redundancy issues. Moving into operations also presents modelers with an opportunity to assess their models performance over calculation intervals unprecedented duration. A key collaboration in the transition of models to operation is the discussion between forecasters and developers on what forecast parameters can accurately be predicted by the current generation of numerical models. This collaboration naturally includes a discussion of the definition of the best metrics to be used in quantitatively assessing performance.

  14. Comparative Election Forecasting: Further Insights from Synthetic Models

    OpenAIRE

    Michael S. Lewis-Beck; Dassonneville, Ruth

    2015-01-01

    As an enterprise, election forecasting has spread and grown. Initial work began in the 1980s in the United States, eventually travelling to Western Europe, where it finds a current outlet in the most of the region’s democracies. However, that work has been confined to traditional approaches – statistical modeling or poll-watching. We import a new approach, which we call synthetic modeling. These forecasts come from hybrid models blending structural knowledge with contemporary p...

  15. A model to forecast magma chamber rupture

    Science.gov (United States)

    Browning, John; Drymoni, Kyriaki; Gudmundsson, Agust

    2016-04-01

    An understanding of the amount of magma available to supply any given eruption is useful for determining the potential eruption magnitude and duration. Geodetic measurements and inversion techniques are often used to constrain volume changes within magma chambers, as well as constrain location and depth, but such models are incapable of calculating total magma storage. For example, during the 2012 unrest period at Santorini volcano, approximately 0.021 km3 of new magma entered a shallow chamber residing at around 4 km below the surface. This type of event is not unusual, and is in fact a necessary condition for the formation of a long-lived shallow chamber. The period of unrest ended without culminating in eruption, i.e the amount of magma which entered the chamber was insufficient to break the chamber and force magma further towards the surface. Using continuum-mechanics and fracture-mechanics principles, we present a model to calculate the amount of magma contained at shallow depth beneath active volcanoes. Here we discuss our model in the context of Santorini volcano, Greece. We demonstrate through structural analysis of dykes exposed within the Santorini caldera, previously published data on the volume of recent eruptions, and geodetic measurements of the 2011-2012 unrest period, that the measured 0.02% increase in volume of Santorini's shallow magma chamber was associated with magmatic excess pressure increase of around 1.1 MPa. This excess pressure was high enough to bring the chamber roof close to rupture and dyke injection. For volcanoes with known typical extrusion and intrusion (dyke) volumes, the new methodology presented here makes it possible to forecast the conditions for magma-chamber failure and dyke injection at any geodetically well-monitored volcano.

  16. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  17. Performance Assessment of Hydrological Models Considering Acceptable Forecast Error Threshold

    Directory of Open Access Journals (Sweden)

    Qianjin Dong

    2015-11-01

    Full Text Available It is essential to consider the acceptable threshold in the assessment of a hydrological model because of the scarcity of research in the hydrology community and errors do not necessarily cause risk. Two forecast errors, including rainfall forecast error and peak flood forecast error, have been studied based on the reliability theory. The first order second moment (FOSM and bound methods are used to identify the reliability. Through the case study of the Dahuofang (DHF Reservoir, it is shown that the correlation between these two errors has great influence on the reliability index of hydrological model. In particular, the reliability index of the DHF hydrological model decreases with the increasing correlation. Based on the reliability theory, the proposed performance evaluation framework incorporating the acceptable forecast error threshold and correlation among the multiple errors can be used to evaluate the performance of a hydrological model and to quantify the uncertainties of a hydrological model output.

  18. Forecast of useful energy for the TIMES-Norway model

    Energy Technology Data Exchange (ETDEWEB)

    Rosenberg, Eva

    2012-07-25

    A regional forecast of useful energy demand in seven Norwegian regions is calculated based on an earlier work with a national forecast. This forecast will be input to the energy system model TIMES-Norway and analyses will result in forecasts of energy use of different energy carriers with varying external conditions (not included in this report). The forecast presented here describes the methodology used and the resulting forecast of useful energy. lt is based on information of the long-term development of the economy by the Ministry of Finance, projections of population growths from Statistics Norway and several other studies. The definition of a forecast of useful energy demand is not absolute, but depends on the purpose. One has to be careful not to include parts that are a part of the energy system model, such as energy efficiency measures. In the forecast presented here the influence of new building regulations and the prohibition of production of incandescent light bulbs in EU etc. are included. Other energy efficiency measures such as energy management, heat pumps, tightening of leaks etc. are modelled as technologies to invest in and are included in the TIMES-Norway model. The elasticity between different energy carriers are handled by the TIMES-Norway model and some elasticity is also included as the possibility to invest in energy efficiency measures. The forecast results in an increase of the total useful energy from 2006 to 2050 by 18 o/o. The growth is expected to be highest in the regions South and East. The industry remains at a constant level in the base case and increased or reduced energy demand is analysed as different scenarios with the TIMES-Norway model. The most important driver is the population growth. Together with the assumptions made it results in increased useful energy demand in the household and service sectors of 25 o/o and 57 % respectively.(au)

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

    Energy Technology Data Exchange (ETDEWEB)

    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, 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.

  20. A refined fuzzy time series model for stock market forecasting

    Science.gov (United States)

    Jilani, Tahseen Ahmed; Burney, Syed Muhammad Aqil

    2008-05-01

    Time series models have been used to make predictions of stock prices, academic enrollments, weather, road accident casualties, etc. In this paper we present a simple time-variant fuzzy time series forecasting method. The proposed method uses heuristic approach to define frequency-density-based partitions of the universe of discourse. We have proposed a fuzzy metric to use the frequency-density-based partitioning. The proposed fuzzy metric also uses a trend predictor to calculate the forecast. The new method is applied for forecasting TAIEX and enrollments’ forecasting of the University of Alabama. It is shown that the proposed method work with higher accuracy as compared to other fuzzy time series methods developed for forecasting TAIEX and enrollments of the University of Alabama.

  1. Forecasting electricity usage using univariate time series models

    Science.gov (United States)

    Hock-Eam, Lim; Chee-Yin, Yip

    2014-12-01

    Electricity is one of the important energy sources. A sufficient supply of electricity is vital to support a country's development and growth. Due to the changing of socio-economic characteristics, increasing competition and deregulation of electricity supply industry, the electricity demand forecasting is even more important than before. It is imperative to evaluate and compare the predictive performance of various forecasting methods. This will provide further insights on the weakness and strengths of each method. In literature, there are mixed evidences on the best forecasting methods of electricity demand. This paper aims to compare the predictive performance of univariate time series models for forecasting the electricity demand using a monthly data of maximum electricity load in Malaysia from January 2003 to December 2013. Results reveal that the Box-Jenkins method produces the best out-of-sample predictive performance. On the other hand, Holt-Winters exponential smoothing method is a good forecasting method for in-sample predictive performance.

  2. Improving statistical forecasts of seasonal streamflows using hydrological model output

    Directory of Open Access Journals (Sweden)

    D. E. Robertson

    2013-02-01

    Full Text Available Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1 when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2 when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3 when the initial catchment condition is near saturation intermittently throughout the historical record.

  3. Improving statistical forecasts of seasonal streamflows using hydrological model output

    Science.gov (United States)

    Robertson, D. E.; Pokhrel, P.; Wang, Q. J.

    2013-02-01

    Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

  4. Forecasting Models for Hydropower Unit Stability Using LS-SVM

    Directory of Open Access Journals (Sweden)

    Liangliang Qiao

    2015-01-01

    Full Text Available This paper discusses a least square support vector machine (LS-SVM approach for forecasting stability parameters of Francis turbine unit. To achieve training and testing data for the models, four field tests were presented, especially for the vibration in Y-direction of lower generator bearing (LGB and pressure in draft tube (DT. A heuristic method such as a neural network using Backpropagation (NNBP is introduced as a comparison model to examine the feasibility of forecasting performance. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to the NNBP, which is of significant importance to better monitor the unit safety and potential faults diagnosis.

  5. A forecasting model of gaming revenues in Clark County, Nevada

    Energy Technology Data Exchange (ETDEWEB)

    Edwards, B.; Bando, A.; Bassett, G.; Rosen, A. [Argonne National Lab., IL (United States); Carlson, J.; Meenan, C. [Science Applications International Corp., Las Vegas, NV (United States)

    1992-04-01

    This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry, an identify the exogenous variables that affect gaming revenues. This model will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming related economic activity resulting from future events like the siting of a permanent high-level radioactive waste repository at Yucca Mountain.

  6. Medium Range Forecast (MRF) and Nested Grid Model (NGM)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Nested Grid Model (NGM) and Medium Range Forecast (MRF) Archive is historical digital data set DSI-6140, archived at the NOAA National Centers for Environmental...

  7. Formation of an Integrated Stock Price Forecast Model in Lithuania

    Directory of Open Access Journals (Sweden)

    Audrius Dzikevičius

    2016-12-01

    Full Text Available Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.

  8. Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

    Science.gov (United States)

    Bennett, James C.; Wang, Q. J.; Li, Ming; Robertson, David E.; Schepen, Andrew

    2016-10-01

    We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modeling is accounted for with a three-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12 months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12 month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers.

  9. Forecasting, Forecasting

    Science.gov (United States)

    Michael A. Fosberg

    1987-01-01

    Future improvements in the meteorological forecasts used in fire management will come from improvements in three areas: observational systems, forecast techniques, and postprocessing of forecasts and better integration of this information into the fire management process.

  10. Modeling And Forecasting Exchange-Rate Shocks

    OpenAIRE

    Andreou, A. S.; Zombanakis, George A.; Likothanassis, S. D.; Georgakopoulos, E.

    1998-01-01

    This paper considers the extent to which the application of neural networks methodology can be used in order to forecast exchange-rate shocks. Four major foreign currency exchange rates against the Greek Drachma as well as the overnight interest rate in the Greek market are employed in an attempt to predict the extent to which the local currency may be suffering an attack. The forecasting is extended to the estimation of future exchange rates and interest rates. The MLP proved to be highly ...

  11. Spatio-temporal modeling for real-time ozone forecasting.

    Science.gov (United States)

    Paci, Lucia; Gelfand, Alan E; Holland, David M

    2013-05-01

    The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.

  12. A Guide to Modern Econometrics

    NARCIS (Netherlands)

    M.J.C.M. Verbeek (Marno)

    2004-01-01

    textabstractA Guide to Modern Econometrics is a new textbook published by John Wiley and Sons. It covers a wide range of topics in applied econometrics in a concise and intuitive way. Some distinctive features: Emphasis on empirical relevance and intuition, paying attention to the links between alt

  13. Modelling and forecasting Turkish residential electricity demand

    Energy Technology Data Exchange (ETDEWEB)

    Dilaver, Zafer, E-mail: Z.dilaver@surrey.ac.uk [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom); The Republic of Turkey Prime Ministry, PK 06573, Ankara (Turkey); Hunt, Lester C [Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, GU2 7XH United Kingdom (United Kingdom)

    2011-06-15

    This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being -0.09 and -0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008. - Research Highlights: > Estimated short run and long run expenditure elasticities of 0.38 and 1.57, respectively. > Estimated short run and long run price elasticities of -0.09 and -0.38, respectively. > Estimated UEDT has increasing (i.e. energy using) and decreasing (i.e. energy saving) periods. > Predicted Turkish residential electricity demand between 48 and 80 TWh in 2020.

  14. A New Method for Grey Forecasting Model Group

    Institute of Scientific and Technical Information of China (English)

    李峰; 王仲东; 宋中民

    2002-01-01

    In order to describe the characteristics of some systems, such as the process of economic and product forecasting, a lot of discrete data may be used. Although they are discrete, the inside law can be-founded by some methods. For a series that the discrete degree is large and the integrated tendency is ascending, a new method for grey forecasting model group is given by the grey system theory. The method is that it firstly transforms original data, chooses some clique values and divides original data into groups by different clique values; then, it establishes non-equigap GM(1, 1) model for different groups and searches forecasting area of original data by the solution of model. At the end of the paper, the result of reliability of forecasting value is obtained. It is shown that the method is feasible.

  15. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  16. Modeling and forecasting the peak flows of a river

    Directory of Open Access Journals (Sweden)

    Mario Lefebvre

    2002-01-01

    Full Text Available A stochastic model is found for the value of the peak flows of the Mistassibi river in Québec, Canada, when the river is in spate. Next, the objective is to forecast the value of the coming peak flow about four days in advance, when the flow begins to show a marked increase. Both the stochastic model proposed in the paper and a model based on linear regression are used to produce the forecasts. The quality of the forecasts is assessed by considering the standard errors and the peak criterion. The forecasts are much more accurate than those obtained by taking the mean value of the previous peak flows.

  17. Short-Termed Integrated Forecasting System: 1993 Model documentation report

    Energy Technology Data Exchange (ETDEWEB)

    1993-05-01

    The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the US Energy Department (DOE) developed the STIFS model to generate short-term (up to 8 quarters), monthly forecasts of US supplies, demands, imports exports, stocks, and prices of various forms of energy. The models that constitute STIFS generate forecasts for a wide range of possible scenarios, including the following ones done routinely on a quarterly basis: A base (mid) world oil price and medium economic growth. A low world oil price and high economic growth. A high world oil price and low economic growth. This report is written for persons who want to know how short-term energy markets forecasts are produced by EIA. The report is intended as a reference document for model analysts, users, and the public.

  18. An assessment of econometric models applied to fossil fuel power generation; Un'analisi critica dell'applicazione dei modelli econometrici alla generazione termoelettrica

    Energy Technology Data Exchange (ETDEWEB)

    Gracceva, F.; Quercioli, R. [ENEA, Funzione Centrale Studi, Centro Ricerche Casaccia, Rome (Italy)

    2001-07-01

    The main purpose of this report is to provide a general view of those studies, in which the econometric approach is applied to the selection of fuel in fossil fired power generation, focusing the attention to the key role played by the fuel prices. The report consists of a methodological analysis and a survey of the studies available in literature. The methodological analysis allows to assess the adequateness of the econometric approach, in the electrical power utilities policy. With this purpose, the fundamentals of microeconomics, which are the basis of the econometric models, are pointed out and discussed, and then the hypotheses, which are needed to be assumed for complying the economic theory, are verified in their actual implementation in the power generation sector. The survey of the available studies provides a detailed description of the Translog and Logit models, and the results achieved with their application. From these results, the estimated models show to fit the data with good approximation, a certain degree of interfuel substitution and a meaningful reaction to prices on demand side. [Italian] In questo rapporto viene tracciato un quadro generale degli studi che utilizzano modelli econometrici per analizzare la scelta dei combustibili nella termogenerazione, con particoalre attenzione al ruolo svolto dal prezzo dei combustibili. La trattazione si compone di un'analisi di tipo metodologico e di una rassegna della letteratura. L'analisi metodologica consente di valutare l'adeguatezza dell'approccio econometrico nell'analisi del comportamento delle imprese di generazione elettrica. A tal fine vengono esplicitati e discussi i fondamenti microeconomici su cui poggiano i modelli econometrici, e viene verificata la sussistenza, nel settore termoelettrico, delle ipotesi che e' necessario assumere per soddisfare la teoria economica. La rassegna fornisce invece una descrizione dei modelli translog e logit lineare, ed un

  19. Flood Forecast and Early Warning with High-Resolution Ensemble Rainfall from Numerical Weather Prediction Model

    OpenAIRE

    Yu, Wansik; NAKAKITA, Eiichi; Jung, Kwansue

    2016-01-01

    This paper investigates the applicability of ensemble forecasts of numerical weather prediction (NWP) model for flood forecasting. In this study, 10 km resolution ensemble rainfalls forecast and their downscaled forecasts of 2 km resolution were used in the hydrologic model as input data for flood forecasting and application of flood early warning. Ensemble data consists of 51 members and 48 hr forecast time. Ensemble outputs are verified spatially whether they can produce suitable rainfall p...

  20. Attractor-based models for individual and groups’ forecasting

    Science.gov (United States)

    Astakhova, N. N.; Demidova, L. A.; Kuzovnikov, A. V.; Tishkin, R. V.

    2017-02-01

    In this paper the questions of the attractors’ application in case of the development of the forecasting models on the base of the strictly binary trees have been considered. Usually, these models use the short time series as the training data sequence. The application of the principles of the attractors’ forming on the base of the long time series will allow creating the training data sequence more reasonably. The offered approach to creation of the training data sequence for the forecasting models on the base of the strictly binary trees was applied for the individual and groups’ forecasting of time series. At the same time the problems of one-objective and multiobjective optimization on the base of the modified clonal selection algorithm have been considered. The reviewed examples confirm the efficiency of the attractors’ application in sense of minimization of the used quality indicators of the forecasting models, and also the forecasting errors on 1 – 5 steps forward. Besides, the minimization of time expenditures for the development of the forecasting models is provided.

  1. Coupling meteorological and hydrological models for flood forecasting

    Directory of Open Access Journals (Sweden)

    Bartholmes

    2005-01-01

    Full Text Available This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2. The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994. The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.

  2. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

    CSIR Research Space (South Africa)

    Das, Sonali

    2010-01-01

    Full Text Available This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, the authors forecast house price inflation for five...

  3. A Bayesian Model Committee Approach to Forecasting Global Solar Radiation

    CERN Document Server

    Lauret, Philippe; Muselli, Marc; David, Mathieu; Diagne, Hadja; Voyant, Cyril

    2012-01-01

    This paper proposes to use a rather new modelling approach in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving Average (ARMA) and Neural Network (NN) models are combined to form a model committee. The Bayesian inference is used to affect a probability to each model in the committee. Hence, each model's predictions are weighted by their respective probability. The models are fitted to one year of hourly Global Horizontal Irradiance (GHI) measurements. Another year (the test set) is used for making genuine one hour ahead (h+1) out-of-sample forecast comparisons. The proposed approach is benchmarked against the persistence model. The very first results show an improvement brought by this approach.

  4. A complex autoregressive model and application to monthly temperature forecasts

    Directory of Open Access Journals (Sweden)

    X. Gu

    2005-11-01

    Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.

  5. Forecasting inflation in Montenegro using univariate time series models

    Directory of Open Access Journals (Sweden)

    Milena Lipovina-Božović

    2015-04-01

    Full Text Available The analysis of price trends and their prognosis is one of the key tasks of the economic authorities in each country. Due to the nature of the Montenegrin economy as small and open economy with euro as currency, forecasting inflation is very specific which is more difficult due to low quality of the data. This paper analyzes the utility and applicability of univariate time series models for forecasting price index in Montenegro. Data analysis of key macroeconomic movements in previous decades indicates the presence of many possible determinants that could influence forecasting result. This paper concludes that the forecasting models (ARIMA based only on its own previous values cannot adequately cover the key factors that determine the price level in the future, probably because of the existence of numerous external factors that influence the price movement in Montenegro.

  6. Application of Markov Model in Crude Oil Price Forecasting

    Directory of Open Access Journals (Sweden)

    Nuhu Isah

    2017-08-01

    Full Text Available Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.

  7. Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting

    Science.gov (United States)

    Moorhead, Althea V.

    2017-01-01

    The meteoroid environment is often divided conceptually into meteor showers plus a sporadic background component. The sporadic complex poses the bulk of the risk to spacecraft, but showers can produce significant short-term enhancements of the meteoroid flux. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. Both MEM and the forecast are used by multiple manned spaceflight projects in their meteoroid risk evaluation, and both tools are being revised to incorporate recent meteor velocity, density, and timing measurements. MEM describes the sporadic meteoroid complex and calculates the flux, speed, and directionality of the meteoroid environment relative to a user-supplied spacecraft trajectory, taking the spacecraft's motion into account. MEM is valid in the inner solar system and offers near-Earth and cis-lunar environments. While the current version of MEM offers a nominal meteoroid environment corresponding to a single meteoroid bulk density, the next version of MEMR3 will offer both flux uncertainties and a density distribution in addition to a revised near-Earth environment. We have updated the near-Earth meteor speed distribution and have made the first determination of uncertainty in this distribution. We have also derived a meteor density distribution from the work of Kikwaya et al. (2011). The annual meteor shower forecast takes the form of a report and data tables that can be used in conjunction with an existing MEM assessment. Fluxes are typically quoted to a constant limiting kinetic energy in order to comport with commonly used ballistic limit equations. For the 2017 annual forecast, the MEO substantially revised the list of showers and their characteristics using 14 years of meteor flux measurements from the Canadian Meteor Orbit Radar (CMOR). Defunct or insignificant showers were removed and the temporal profiles of many showers

  8. Hydrological model calibration for enhancing global flood forecast skill

    Science.gov (United States)

    Hirpa, Feyera A.; Beck, Hylke E.; Salamon, Peter; Thielen-del Pozo, Jutta

    2016-04-01

    Early warning systems play a key role in flood risk reduction, and their effectiveness is directly linked to streamflow forecast skill. The skill of a streamflow forecast is affected by several factors; among them are (i) model errors due to incomplete representation of physical processes and inaccurate parameterization, (ii) uncertainty in the model initial conditions, and (iii) errors in the meteorological forcing. In macro scale (continental or global) modeling, it is a common practice to use a priori parameter estimates over large river basins or wider regions, resulting in suboptimal streamflow estimations. The aim of this work is to improve flood forecast skill of the Global Flood Awareness System (GloFAS; www.globalfloods.eu), a grid-based forecasting system that produces flood forecast unto 30 days lead, through calibration of the distributed hydrological model parameters. We use a combination of in-situ and satellite-based streamflow data for automatic calibration using a multi-objective genetic algorithm. We will present the calibrated global parameter maps and report the forecast skill improvements achieved. Furthermore, we discuss current challenges and future opportunities with regard to global-scale early flood warning systems.

  9. Modeling for Growth and Forecasting of Pulse Production in Bangladesh

    Directory of Open Access Journals (Sweden)

    Niaz Md. FarhatRahman

    2013-05-01

    Full Text Available The present study was carried out to estimate growth pattern and examine the best ARIMA model to efficiently forecasting pigeon pea, chickpea and field pea pulse production in Bangladesh. It appeared that the time series data for pigeon pea, chickpea and field pea were 1st order homogenous stationary. Two types of models namely Box-Jenkins type Autoregressive Integrated Moving Average (ARIMA and deterministic type growth models, are examined to identify the best forecasting models for pigeon pea, chickpea and field pea pulse production in Bangladesh. The study revealed that the best models were ARIMA (1, 1 and 1, ARIMA (0, 1 and 0 and ARIMA (1, 1 and 3 for pigeon pea, chickpea and field pea pulse production, respectively. Among the deterministic type growth models, the cubic model is best for pigeon pea, chickpea and field pea pulse production. The analysis indicated that short-term forecasts were more efficient for ARIMA models compared to the deterministic models. The production uncertainty of pulse could be minimized if production were forecasted well and necessary steps were taken against losses. The findings of this study would be more useful for policy makers, researchers as well as producers in order to forecast future national pulse production more accurately in the short run.

  10. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling.

    Science.gov (United States)

    Ye, Hao; Beamish, Richard J; Glaser, Sarah M; Grant, Sue C H; Hsieh, Chih-Hao; Richards, Laura J; Schnute, Jon T; Sugihara, George

    2015-03-31

    It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner-recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts.

  11. Impact of festival factor on electric quantity multiplication forecast model

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This research aims to improve the forecasting precision of electric quantity. It is discovered that the total electricity consumption considerably increased during the Spring Festival by the analysis of the electric quantity time series from 2002 to 2007 in Shandong province. The festival factor is ascertained to be one of the important seasonal factors affecting the electric quantity fluctuations, and the multiplication model for forecasting is improved by introducing corresponding variables and parameters...

  12. Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

    Science.gov (United States)

    2016-06-07

    year. Our goal is to develop an ensemble ocean forecast methodology, using Bayesian Hierarchical Modelling (BHM) tools . The ocean ensemble forecast...from above); i.e. we assume Ut ~ Z Λt1/2. WORK COMPLETED The prototype MFS-Wind-BHM was designed and implemented based on stochastic...coding refinements we implemented on the prototype surface wind BHM. A DWF event in February 2005, in the Gulf of Lions, was identified for reforecast

  13. Interval forecasts of a novelty hybrid model for wind speeds

    OpenAIRE

    Shanshan Qin; Feng Liu; Jianzhou Wang; Yiliao Song

    2015-01-01

    The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values...

  14. Aerosol Radiative Forcing and Weather Forecasts in the ECMWF Model

    Science.gov (United States)

    Bozzo, A.; Benedetti, A.; Rodwell, M. J.; Bechtold, P.; Remy, S.

    2015-12-01

    Aerosols play an important role in the energy balance of the Earth system via direct scattering and absorpiton of short-wave and long-wave radiation and indirect interaction with clouds. Diabatic heating or cooling by aerosols can also modify the vertical stability of the atmosphere and influence weather pattern with potential impact on the skill of global weather prediction models. The Copernicus Atmosphere Monitoring Service (CAMS) provides operational daily analysis and forecast of aerosol optical depth (AOD) for five aerosol species using a prognostic model which is part of the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS). The aerosol component was developed during the research project Monitoring Atmospheric Composition and Climate (MACC). Aerosols can have a large impact on the weather forecasts in case of large aerosol concentrations as found during dust storms or strong pollution events. However, due to its computational burden, prognostic aerosols are not yet feasible in the ECMWF operational weather forecasts, and monthly-mean climatological fields are used instead. We revised the aerosol climatology used in the operational ECMWF IFS with one derived from the MACC reanalysis. We analyse the impact of changes in the aerosol radiative effect on the mean model climate and in medium-range weather forecasts, also in comparison with prognostic aerosol fields. The new climatology differs from the previous one by Tegen et al 1997, both in the spatial distribution of the total AOD and the optical properties of each aerosol species. The radiative impact of these changes affects the model mean bias at various spatial and temporal scales. On one hand we report small impacts on measures of large-scale forecast skill but on the other hand details of the regional distribution of aerosol concentration have a large local impact. This is the case for the northern Indian Ocean where the radiative impact of the mineral

  15. Ensemble Forecasting of Tropical Cyclone Motion Using a Baroclinic Model

    Institute of Scientific and Technical Information of China (English)

    Xiaqiong ZHOU; Johnny C.L.CHEN

    2006-01-01

    The purpose of this study is to investigate the effectiveness of two different ensemble forecasting (EF) techniques-the lagged-averaged forecast (LAF) and the breeding of growing modes (BGM). In the BGM experiments, the vortex and the environment are perturbed separately (named BGMV and BGME).Tropical cyclone (TC) motions in two difficult situations are studied: a large vortex interacting with its environment, and an apparent binary interaction. The former is Typhoon Yancy and the latter involves Typhoon Ed and super Typhoon Flo, all occurring during the Tropical Cyclone Motion Experiment TCM-90. The model used is the baroclinic model of the University of New South Wales. The lateral boundary tendencies are computed from atmospheric analysis data. Only the relative skill of the ensemble forecast mean over the control run is used to evaluate the effectiveness of the EF methods, although the EF technique is also used to quantify forecast uncertainty in some studies. In the case of Yancy, the ensemble mean forecasts of each of the three methodologies are better than that of the control, with LAF being the best. The mean track of the LAF is close to the best track, and it predicts landfall over Taiwan. The improvements in LAF and the full BGM where both the environment and vortex are perturbed suggest the importance of combining the perturbation of the vortex and environment when the interaction between the two is appreciable. In the binary interaction case of Ed and Flo, the forecasts of Ed appear to be insensitive to perturbations of the environment and/or the vortex, which apparently results from erroneous forecasts by the model of the interaction between the subtropical ridge and Ed, as well as from the interaction between the two typhoons, thus reducing the effectiveness of the EF technique. This conclusion is reached through sensitivity experiments on the domain of the model and by adding or eliminating certain features in the model atmosphere. Nevertheless, the

  16. Bias Analysis of Single Equation Based on Econometric Model%基于单方程计量经济模型的贝叶斯分析

    Institute of Scientific and Technical Information of China (English)

    周俊梅; 蒋文江

    2013-01-01

    The Bias problem of statistical inference about the single equation econometric model is studied,According to the prior distribution structure of sample likelihood function of the unknown parameters,normal and Gamma distribution is selected as the prior distribution.The posterior distribution of statistical inference.Finally deduced Bias unknown parameters in two under quadratic loss function estimation.%研究了关于单方程计量经济模型的贝叶斯统计推断问题,即根据样本似然函数的形式构造出未知参数的先验分布,选取正态-Gamma分布为先验分布,对后验分布进行统计推断.最后推导了未知参数在二次损失函数下的贝叶斯估计.

  17. Meteoroid Environment Modeling: The Meteoroid Engineering Model and Shower Forecasting

    Science.gov (United States)

    Moorhead, Althea V.

    2017-01-01

    The meteoroid environment is often divided conceptually into meteor showers and the sporadic meteor background. It is commonly but incorrectly assumed that meteoroid impacts primarily occur during meteor showers; instead, the vast majority of hazardous meteoroids belong to the sporadic complex. Unlike meteor showers, which persist for a few hours to a few weeks, sporadic meteoroids impact the Earth's atmosphere and spacecraft throughout the year. The Meteoroid Environment Office (MEO) has produced two environment models to handle these cases: the Meteoroid Engineering Model (MEM) and an annual meteor shower forecast. The sporadic complex, despite its year-round activity, is not isotropic in its directionality. Instead, their apparent points of origin, or radiants, are organized into groups called "sources". The speed, directionality, and size distribution of these sporadic sources are modeled by the Meteoroid Engineering Model (MEM), which is currently in its second major release version (MEMR2) [Moorhead et al., 2015]. MEM provides the meteoroid flux relative to a user-provided spacecraft trajectory; it provides the total flux as well as the flux per angular bin, speed interval, and on specific surfaces (ram, wake, etc.). Because the sporadic complex dominates the meteoroid flux, MEM is the most appropriate model to use in spacecraft design. Although showers make up a small fraction of the meteoroid environment, they can produce significant short-term enhancements of the meteoroid flux. Thus, it can be valuable to consider showers when assessing risks associated with vehicle operations that are brief in duration. To assist with such assessments, the MEO issues an annual forecast that reports meteor shower fluxes as a function of time and compares showers with the time-averaged total meteoroid flux. This permits missions to do quick assessments of the increase in risk posed by meteor showers. Section II describes MEM in more detail and describes our current efforts

  18. Optimization of multi-model ensemble forecasting of typhoon waves

    Directory of Open Access Journals (Sweden)

    Shun-qi Pan

    2016-01-01

    Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.

  19. Wavelet regression model in forecasting crude oil price

    Science.gov (United States)

    Hamid, Mohd Helmie; Shabri, Ani

    2017-05-01

    This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

  20. Functional dynamic factor models with application to yield curve forecasting

    KAUST Repository

    Hays, Spencer

    2012-09-01

    Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.

  1. Model Uncertainty and Exchange Rate Forecasting

    OpenAIRE

    Kouwenberg, Roy; Markiewicz, Agnieszka; Verhoeks, Ralph; Zwinkels, Remco

    2013-01-01

    textabstractWe propose a theoretical framework of exchange rate behavior where investors focus on a subset of economic fundamentals. We find that any adjustment in the set of predictors used by investors leads to changes in the relation between the exchange rate and fundamentals. We test the validity of this framework via a backward elimination rule which captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample forecasting tests show that the backward elimi...

  2. Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

    OpenAIRE

    Haixiang Zang; Lei Fan; Mian Guo; Zhinong Wei; Guoqiang Sun; Li Zhang

    2016-01-01

    Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EE...

  3. Nearest neighbour models for local and regional avalanche forecasting

    Directory of Open Access Journals (Sweden)

    M. Gassner

    2002-01-01

    Full Text Available This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Re-search (SLF. Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.

  4. A review of forecasting models for new products

    Directory of Open Access Journals (Sweden)

    Marta Mas-Machuca

    2014-02-01

    Full Text Available Purpose. The main objective of this article is to present an up-to-date review of new product forecasting techniques. Design/methodology/approach: A systematic review of forecasting journals was carried out using the ISI-Web of Knowledge database. Several articles were retrieved and examined, and forecasting techniques relevant to this study were selected and assessed. Findings: The strengths, weaknesses and applications of the main forecasting models are discussed to examine trends and set future challenges. Research limitations/implications: A theoretical reference framework for forecasting techniques classified into judgmental, consumer/market research, cause-effect and artificial intelligence is proposed. Future research can assess these models qualitatively. Practical implications: Companies are currently motivated to launch new products and thus attract new customers to expand their market share.  In order to reduce uncertainty and risk, many companies go to extra lengths to forecast sales accurately using several techniques. Originality/value: This article outlines new lines of research on the improvement of new product performance which will aid managers in decision making and allow companies to sustain their competitive advantages in this challenging world.

  5. Validation of Model Forecasts of the Ambient Solar Wind

    Science.gov (United States)

    Macneice, P. J.; Hesse, M.; Kuznetsova, M. M.; Rastaetter, L.; Taktakishvili, A.

    2009-01-01

    Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the community, sharpened their definitions, and completed a baseline study. We also provide first results from this program of the comparative performance of the MHD models available at the CCMC against that of the Wang-Sheeley-Arge (WSA) model. An important goal of this effort is to provide a consistent validation to all available models. Clearly exposing the relative strengths and weaknesses of the different models will enable forecasters to craft more reliable ensemble forecasting strategies. Models of the ambient solar wind are developing rapidly as a result of improvements in data supply, numerical techniques, and computing resources. It is anticipated that in the next five to ten years, the MHD based models will supplant semi-empirical potential based models such as the WSA model, as the best available forecast models. We anticipate that this validation effort will track this evolution and so assist policy makers in gauging the value of past and future investment in modeling support.

  6. Forecasting the Euro exchange rate using vector error correction models

    NARCIS (Netherlands)

    Aarle, B. van; Bos, M.; Hlouskova, J.

    2000-01-01

    Forecasting the Euro Exchange Rate Using Vector Error Correction Models. — This paper presents an exchange rate model for the Euro exchange rates of four major currencies, namely the US dollar, the British pound, the Japanese yen and the Swiss franc. The model is based on the monetary approach of ex

  7. Improved forecasting of thermospheric densities using multi-model ensembles

    Science.gov (United States)

    Elvidge, Sean; Godinez, Humberto C.; Angling, Matthew J.

    2016-07-01

    This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertainties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere-Ionosphere Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), and Global Ionosphere-Thermosphere Model (GITM) have been used to construct the MME. As well as comparisons between the MMEs and the "standard" runs of the model, the MME densities have been propagated forward in time using the TIE-GCM. It is shown that thermospheric forecasts of up to 6 h, using the MME, have a reduction in the root mean square error of greater than 60 %. The paper also highlights differences in model performance between times of solar minimum and maximum.

  8. Evaluation of the performance of DIAS ionospheric forecasting models

    Directory of Open Access Journals (Sweden)

    Tsagouri Ioanna

    2011-08-01

    Full Text Available Nowcasting and forecasting ionospheric products and services for the European region are regularly provided since August 2006 through the European Digital upper Atmosphere Server (DIAS, http://dias.space.noa.gr. Currently, DIAS ionospheric forecasts are based on the online implementation of two models: (i the solar wind driven autoregression model for ionospheric short-term forecast (SWIF, which combines historical and real-time ionospheric observations with solar-wind parameters obtained in real time at the L1 point from NASA ACE spacecraft, and (ii the geomagnetically correlated autoregression model (GCAM, which is a time series forecasting method driven by a synthetic geomagnetic index. In this paper we investigate the operational ability and the accuracy of both DIAS models carrying out a metrics-based evaluation of their performance under all possible conditions. The analysis was established on the systematic comparison between models’ predictions with actual observations obtained over almost one solar cycle (1998–2007 at four European ionospheric locations (Athens, Chilton, Juliusruh and Rome and on the comparison of the models’ performance against two simple prediction strategies, the median- and the persistence-based predictions during storm conditions. The results verify operational validity for both models and quantify their prediction accuracy under all possible conditions in support of operational applications but also of comparative studies in assessing or expanding the current ionospheric forecasting capabilities.

  9. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  10. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an Adaptiv

  11. The Application of Combination Forecasting Model in Forecasting the Total Power of Agricultural Machinery in Heilongjiang Province

    Institute of Scientific and Technical Information of China (English)

    Xiaoling; HAO; Ruixia; SUO

    2015-01-01

    Agricultural machinery total power is an important index to reflect and evaluate the level of agricultural mechanization. Firstly,we respectively made use of exponential model,grey forecasting and BP neural network to construct models depending on historical data of agricultural machinery total power of Heilongjiang Province; secondly,we constructed the combined forecasting models that respectively based on divergence coefficient method,quadratic programming and weight distribution of Shapley value. Fitting results showed that the various combination forecasting model is superior to the single models. Finally,we applied the combination forecasting model which based on the weight distribution method of Shapley value to forecast Heilongjiang agricultural machinery total power,and it would provide some reference to the development and program for power of agriculture machinery.

  12. Forecasting Performance of Asymmetric GARCH Stock Market Volatility Models

    Directory of Open Access Journals (Sweden)

    Hojin Lee

    2009-12-01

    Full Text Available We investigate the asymmetry between positive and negative returns in their effect on conditional variance of the stock market index and incorporate the characteristics to form an out-of-sample volatility forecast. Contrary to prior evidence, however, the results in this paper suggest that no asymmetric GARCH model is superior to basic GARCH(1,1 model. It is our prior knowledge that, for equity returns, it is unlikely that positive and negative shocks have the same impact on the volatility. In order to reflect this intuition, we implement three diagnostic tests for volatility models: the Sign Bias Test, the Negative Size Bias Test, and the Positive Size Bias Test and the tests against the alternatives of QGARCH and GJR-GARCH. The asymmetry test results indicate that the sign and the size of the unexpected return shock do not influence current volatility differently which contradicts our presumption that there are asymmetric effects in the stock market volatility. This result is in line with various diagnostic tests which are designed to determine whether the GARCH(1,1 volatility estimates adequately represent the data. The diagnostic tests in section 2 indicate that the GARCH(1,1 model for weekly KOSPI returns is robust to the misspecification test. We also investigate two representative asymmetric GARCH models, QGARCH and GJR-GARCH model, for our out-of-sample forecasting performance. The out-of-sample forecasting ability test reveals that no single model is clearly outperforming. It is seen that the GJR-GARCH and QGARCH model give mixed results in forecasting ability on all four criteria across all forecast horizons considered. Also, the predictive accuracy test of Diebold and Mariano based on both absolute and squared prediction errors suggest that the forecasts from the linear and asymmetric GARCH models need not be significantly different from each other.

  13. An interdisciplinary approach for earthquake modelling and forecasting

    Science.gov (United States)

    Han, P.; Zhuang, J.; Hattori, K.; Ogata, Y.

    2016-12-01

    Earthquake is one of the most serious disasters, which may cause heavy casualties and economic losses. Especially in the past two decades, huge/mega earthquakes have hit many countries. Effective earthquake forecasting (including time, location, and magnitude) becomes extremely important and urgent. To date, various heuristically derived algorithms have been developed for forecasting earthquakes. Generally, they can be classified into two types: catalog-based approaches and non-catalog-based approaches. Thanks to the rapid development of statistical seismology in the past 30 years, now we are able to evaluate the performances of these earthquake forecast approaches quantitatively. Although a certain amount of precursory information is available in both earthquake catalogs and non-catalog observations, the earthquake forecast is still far from satisfactory. In most case, the precursory phenomena were studied individually. An earthquake model that combines self-exciting and mutually exciting elements was developed by Ogata and Utsu from the Hawkes process. The core idea of this combined model is that the status of the event at present is controlled by the event itself (self-exciting) and all the external factors (mutually exciting) in the past. In essence, the conditional intensity function is a time-varying Poisson process with rate λ(t), which is composed of the background rate, the self-exciting term (the information from past seismic events), and the external excitation term (the information from past non-seismic observations). This model shows us a way to integrate the catalog-based forecast and non-catalog-based forecast. Against this background, we are trying to develop a new earthquake forecast model which combines catalog-based and non-catalog-based approaches.

  14. Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics

    CERN Document Server

    Scheuerer, Michael

    2013-01-01

    Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method that generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution that is left-censored at zero. This distribution permits modelling precipitation on the original scale without prior transformation of the data. A closed form expression for its continuous rank probability score can be derived and permits computationally efficient model fitting. We discuss an extension of our approach that incorporates further statistics characterizing the spatial variability of precipitation amounts in the vicinity of the location of interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble prediction system operated by the Germa...

  15. Forecasting relativistic electron flux using dynamic multiple regression models

    Directory of Open Access Journals (Sweden)

    H.-L. Wei

    2011-02-01

    Full Text Available The forecast of high energy electron fluxes in the radiation belts is important because the exposure of modern spacecraft to high energy particles can result in significant damage to onboard systems. A comprehensive physical model of processes related to electron energisation that can be used for such a forecast has not yet been developed. In the present paper a systems identification approach is exploited to deduce a dynamic multiple regression model that can be used to predict the daily maximum of high energy electron fluxes at geosynchronous orbit from data. It is shown that the model developed provides reliable predictions.

  16. Forecasting coconut production in the Philippines with ARIMA model

    Science.gov (United States)

    Lim, Cristina Teresa

    2015-02-01

    The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

  17. Challenging Issues on fog forecast with a three-dimensional fog forecast model

    Science.gov (United States)

    Masbou, M.

    2012-12-01

    Fog has a significant impact on economical aspect (traffic management and safety) as well as on environmental issues (fresh water source for the population and the biosphere in arid region). However, reliable fog and visibility forecasts stay challenging issue. Fog is generally a small scale phenomenon which is mostly affected by local advective transport, radiation, topography, vegetation, turbulent mixing at the surface as well as its microphysical structure. In order to consider these intertwined processes, the three-dimensional fog forecast model, COSMO-FOG, with a high vertical resolution with different microphysical complexity has been developed. This model includes a microphysical parameterisation based on the one-dimensional fog forecast model. The implementation of the cloud water droplets as a new prognostic variable allows a detailed definition of the sedimentation processes and the variations in visibility. Moreover, the turbulence scheme, based on a Mellor-Yamada 2.5 order and a closure of a 2nd order has been modified to improve the model behaviour in case of a stable atmosphere structure, occurring typically during night radiative fog episodes. The potential of COSMO-FOG will be presented in some realistic fog situations (flat, bumpy and complex terrain). The fog spatial extension will be compared with MSG satellite products for fog and low cloud. The interplays between dynamical, thermodynamical patterns and the soil-atmosphere interactions will be presented.

  18. Post-processing of Solar Irradiance Forecasts from WRF Model at Reunion Island

    OpenAIRE

    Diagne, Hadja Maïmouna; David, Mathieu; Boland, John; Schmutz, Nicolas; Lauret, Philippe

    2014-01-01

    International audience; An efficient use of solar energy production requires reliable forecast information on surface solar irradiance. This article aims at providing a model output statistics (MOS) method of improving solar irradiance forecasts from Weather Research and Forecasting (WRF) Model.The WRF model was used to produce one year of day ahead solar irradiance forecasts covering Reunion Island with an horizontal resolution of 3 km. These forecasts are refined with a Kalman filter using ...

  19. Appendix to part 3: examining the macroeconomic effects of curbing CO{sub 2} emissions with the Project LINK world econometric model

    Energy Technology Data Exchange (ETDEWEB)

    Li, H.; Pauly, P.; Ruffing, K.G. [University of Toronto, Toronto, ON (Canada). Inst. for Policy Analysis

    1997-12-31

    The authors identify two problems in most simulations of the impact of carbon taxes using macroeconomic models and energy models. Macroeconomic models treat the level of economic activity as an exogenous variable and energy models do so for energy demand. Secondly, many models assume a constant pattern of international trade and exogenous energy prices. These limit the model`s ability to evaluate a carbon tax. The simulation using the world econometric model of Project LINK with the trace gas accounting system (TGAS) aims at endogenously generating these impacts of a carbon tax. A simulation is presented of a uniform 40 US dollar carbon tax with an endogenous oil price response where the oil price will be about 3 US dollars per barrel below the original scenarios by 2000 and the emission reductions are lower by 1.5-4.7%. The authors conclude that a unilateral G7 carbon tax would reduce CO{sub 2} emissions in these countries. Elimination or reduction of the negative activity effects of a carbon tax is possible through the recycling of revenues and parallel stabilizing policies. 24 refs., 3 tabs.

  20. Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

    Science.gov (United States)

    Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

    2014-01-01

    Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

  1. Fuzzy temporal logic based railway passenger flow forecast model.

    Science.gov (United States)

    Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

    2014-01-01

    Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.

  2. Forecasting wind-driven wildfires using an inverse modelling approach

    Directory of Open Access Journals (Sweden)

    O. Rios

    2013-12-01

    Full Text Available A technology able to rapidly forecast wildlfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the on-going fire. The article at hand presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and a forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the high capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event. This work opens the door to further advances framework and more sophisticated models while keeping the computational time suitable for operativeness.

  3. Integer-valued Lévy processes and low latency financial econometrics

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole; Pollard, David G.; Shephard, Neil

    Motivated by features of low latency data in financial econometrics we study in detail integervalued Lévy processes as the basis of price processes for high frequency econometrics. We propose using models built out of the difference of two subordinators. We apply these models in practice to low...... latency data for a variety of different types of futures contracts.futures markets, high frequency econometrics, low latency data, negative binomial, Skellam, tempered stable...

  4. Forecasting in marketing

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans)

    2004-01-01

    textabstractWith the advent of advanced data collection techniques, there is an increased interest in using econometric models to support decisions in marketing. Due to the sometimes specific nature of variables in marketing, the discipline uses econometric models that are rarely, if ever,

  5. Forecasting in marketing

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans)

    2004-01-01

    textabstractWith the advent of advanced data collection techniques, there is an increased interest in using econometric models to support decisions in marketing. Due to the sometimes specific nature of variables in marketing, the discipline uses econometric models that are rarely, if ever, use

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

    Science.gov (United States)

    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

  7. Comparison of Wind energy production forecasts, in terms of errors and economic losses

    Science.gov (United States)

    Mestre, O.; Texier, O.; Girard, N.; Usaola, J.; Bantegnie, P.

    2009-04-01

    We compare 6 forecasts productions models on two windfarms located in France. The evaluation is made in terms of root mean square errors. The power production forecasts are the products of both physical and statistical models and cover a period of 6 months. We show that the economic performances of those models can be improved using econometric approaches, where we to minimize the cost induced by the forecast error instead of minimizing the forecast error itself. This technique relies on state of the art non-parametric estimators of conditional probability distribution functions (cpdf) of energy production at a wind farm, given the wind speed forecasts of a deterministic meteorological model. In this case, no assumption is made about the shape of the underlying laws. The economical benefits of ensemble versus deterministic wind speed forecasts are also assessed.

  8. Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)

    OpenAIRE

    Pappenberger, F.; K. J. Beven; N. M. Hunter; Bates, P. D.; B. T. Gouweleeuw; Thielen, J.; A. P. J. De De Roo

    2005-01-01

    International audience; The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall-runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessa...

  9. Lake Michigan lake trout PCB model forecast post audit

    Science.gov (United States)

    Scenario forecasts for total PCBs in Lake Michigan (LM) lake trout were conducted using the linked LM2-Toxics and LM Food Chain models, supported by a suite of additional LM models. Efforts were conducted under the Lake Michigan Mass Balance Study and the post audit represents th...

  10. Exchange Rate Forecasting Using Entropy Optimized Multivariate Wavelet Denoising Model

    Directory of Open Access Journals (Sweden)

    Kaijian He

    2014-01-01

    Full Text Available Exchange rate is one of the key variables in the international economics and international trade. Its movement constitutes one of the most important dynamic systems, characterized by nonlinear behaviors. It becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulation and global integration worldwide. Facing the increasingly diversified and more integrated market environment, the forecasting model in the exchange markets needs to address the individual and interdependent heterogeneity. In this paper, we propose the heterogeneous market hypothesis- (HMH- based exchange rate modeling methodology to model the micromarket structure. Then we further propose the entropy optimized wavelet-based forecasting algorithm under the proposed methodology to forecast the exchange rate movement. The multivariate wavelet denoising algorithm is used to separate and extract the underlying data components with distinct features, which are modeled with multivariate time series models of different specifications and parameters. The maximum entropy is introduced to select the best basis and model parameters to construct the most effective forecasting algorithm. Empirical studies in both Chinese and European markets have been conducted to confirm the significant performance improvement when the proposed model is tested against the benchmark models.

  11. Periodic Integration: Further Results on Model Selection and Forecasting

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1996-01-01

    textabstractThis paper considers model selection and forecasting issues in two closely related models for nonstationary periodic autoregressive time series [PAR]. Periodically integrated seasonal time series [PIAR] need a periodic differencing filter to remove the stochastic trend. On the other

  12. Developing the concept of Sustainable Peace using Econometrics and scenarios granting Sustainable Peace in Colombia by year 2019

    OpenAIRE

    Gomez-Sorzano, Gustavo

    2007-01-01

    This paper belongs to my research program on violence and terrorism started in 1993, as a consequence of the growing concern regarding the increase in Colombian violence, and especially for its escalation during the 1990’s. After 14 years of research, particularly after developing a model of cyclical terrorist murder in Colombia 1950-2004, forecasts 2005-2019 (Gómez-Sorzano 2005, http://mpra.ub.uni-muenchen.de/134/01/MPRA_paper_134.pdf), the econometrics of violence, terrorism, and scenario...

  13. Weather modeling and forecasting of PV systems operation

    CERN Document Server

    Paulescu, Marius; Gravila, Paul; Badescu, Viorel

    2012-01-01

    In the past decade, there has been a substantial increase of grid-feeding photovoltaic applications, thus raising the importance of solar electricity in the energy mix. This trend is expected to continue and may even increase. Apart from the high initial investment cost, the fluctuating nature of the solar resource raises particular insertion problems in electrical networks. Proper grid managing demands short- and long-time forecasting of solar power plant output. Weather modeling and forecasting of PV systems operation is focused on this issue. Models for predicting the state of the sky, nowc

  14. An integrated model for a forecasting model of the electric power market in the long term; Um modelo integrado de previsao do mercado de energia eletrica a longo prazo

    Energy Technology Data Exchange (ETDEWEB)

    Faria, Sergio Nilo Gomes

    1993-07-01

    A proposal for a forecasting model of the electricity market which, in methodological terms, is based on classic econometric formulations - evaluation of income and price elasticities. The electricity demand for some industrial sectors is dealt with in a desegregated way, in order to capture its dependence on the economic activity of these sectors is presented. The proposal of this thesis differs from the usual methodology as far as evaluating the impacts of the energy demand forecast, conformed to well defined macroeconomics and tariff policy assumptions, on the expansion of the power system as a whole, and, particularly, on the financial situation of the power sector. The motivation for the study was the need for a new methodological tool, broad, but streamlined enough to allow widespread assessments of alternative development scenarios associated to different economic and politic contexts, taking into account the main uncertainties present in the several planning stages. (author)

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

    Science.gov (United States)

    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.

  16. An updated subgrid orographic parameterization for global atmospheric forecast models

    Science.gov (United States)

    Choi, Hyun-Joo; Hong, Song-You

    2015-12-01

    A subgrid orographic parameterization (SOP) is updated by including the effects of orographic anisotropy and flow-blocking drag (FBD). The impact of the updated SOP on short-range forecasts is investigated using a global atmospheric forecast model applied to a heavy snowfall event over Korea on 4 January 2010. When the SOP is updated, the orographic drag in the lower troposphere noticeably increases owing to the additional FBD over mountainous regions. The enhanced drag directly weakens the excessive wind speed in the low troposphere and indirectly improves the temperature and mass fields over East Asia. In addition, the snowfall overestimation over Korea is improved by the reduced heat fluxes from the surface. The forecast improvements are robust regardless of the horizontal resolution of the model between T126 and T510. The parameterization is statistically evaluated based on the skill of the medium-range forecasts for February 2014. For the medium-range forecasts, the skill improvements of the wind speed and temperature in the low troposphere are observed globally and for East Asia while both positive and negative effects appear indirectly in the middle-upper troposphere. The statistical skill for the precipitation is mostly improved due to the improvements in the synoptic fields. The improvements are also found for seasonal simulation throughout the troposphere and stratosphere during boreal winter.

  17. Ionospheric scintillation forecasting model based on NN-PSO technique

    Science.gov (United States)

    Sridhar, M.; Venkata Ratnam, D.; Padma Raju, K.; Sai Praharsha, D.; Saathvika, K.

    2017-09-01

    The forecasting and modeling of ionospheric scintillation effects are crucial for precise satellite positioning and navigation applications. In this paper, a Neural Network model, trained using Particle Swarm Optimization (PSO) algorithm, has been implemented for the prediction of amplitude scintillation index (S4) observations. The Global Positioning System (GPS) and Ionosonde data available at Darwin, Australia (12.4634° S, 130.8456° E) during 2013 has been considered. The correlation analysis between GPS S4 and Ionosonde drift velocities (hmf2 and fof2) data has been conducted for forecasting the S4 values. The results indicate that forecasted S4 values closely follow the measured S4 values for both the quiet and disturbed conditions. The outcome of this work will be useful for understanding the ionospheric scintillation phenomena over low latitude regions.

  18. Advances in nonmarket valuation econometrics: Spatial heterogeneity in hedonic pricing models and preference heterogeneity in stated preference models

    Science.gov (United States)

    Yoo, Jin Woo

    In my 1st essay, the study explores Pennsylvania residents. willingness to pay for development of renewable energy technologies such as solar power, wind power, biomass electricity, and other renewable energy using a choice experiment method. Principle component analysis identified 3 independent attitude components that affect the variation of preference, a desire for renewable energy and environmental quality and concern over cost. The results show that urban residents have a higher desire for environmental quality and concern less about cost than rural residents and consequently have a higher willingness to pay to increase renewable energy production. The results of sub-sample analysis show that a representative respondent in rural (urban) Pennsylvania is willing to pay 3.8(5.9) and 4.1(5.7)/month for increasing the share of Pennsylvania electricity generated from wind power and other renewable energy by 1 percent point, respectively. Mean WTP for solar and biomass electricity was not significantly different from zero. In my second essay, heterogeneity of individual WTP for various renewable energy technologies is investigated using several different variants of the multinomial logit model: a simple MNL with interaction terms, a latent class choice model, a random parameter mixed logit choice model, and a random parameter-latent class choice model. The results of all models consistently show that respondents. preference for individual renewable technology is heterogeneous, but the degree of heterogeneity differs for different renewable technologies. In general, the random parameter logit model with interactions and a hybrid random parameter logit-latent class model fit better than other models and better capture respondents. heterogeneity of preference for renewable energy. The impact of the land under agricultural conservation easement (ACE) contract on the values of nearby residential properties is investigated using housing sales data in two Pennsylvania

  19. Model uncertainty and Bayesian model averaging in vector autoregressive processes

    NARCIS (Netherlands)

    R.W. Strachan (Rodney); H.K. van Dijk (Herman)

    2006-01-01

    textabstractEconomic forecasts and policy decisions are often informed by empirical analysis based on econometric models. However, inference based upon a single model, when several viable models exist, limits its usefulness. Taking account of model uncertainty, a Bayesian model averaging procedure i

  20. FORECASTING MODEL OF GHG EMISSION IN MANUFACTURING SECTORS OF THAILAND

    Directory of Open Access Journals (Sweden)

    Pruethsan Sutthichaimethee

    2017-01-01

    Full Text Available This study aims to analyze the modeling and forecasting the GHG emission of energy consumption in manufacturing sectors. The scope of the study is to analysis energy consumption and forecasting GHG emission of energy consumption for the next 10 years (2016-2025 and 25 years (2016-2040 by using ARIMAX model from the Input-output table of Thailand. The result shows that iron and steel has the highest value of energy consumption and followed by cement, fluorite, air transport, road freight transport, hotels and places of loading, coal and lignite, petrochemical products, other manufacturing, road passenger transport, respectively. The prediction results show that these models are effective in forecasting by measured by using RMSE, MAE, and MAPE. The results forecast of each model is as follows: 1 Model 1(2,1,1 shows that GHG emission will be increasing steadily and increasing at 25.17% by the year 2025 in comparison to 2016. 2 Model 2 (2,1,2 shows that GHG emission will be rising steadily and increasing at 41.51% by the year 2040 in comparison to 2016.

  1. [Development of forecasting models for fatal road traffic injuries].

    Science.gov (United States)

    Tan, Aichun; Tian, Danping; Huang, Yuanxiu; Gao, Lin; Deng, Xin; Li, Li; He, Qiong; Chen, Tianmu; Hu, Guoqing; Wu, Jing

    2014-02-01

    To develop the forecasting models for fatal road traffic injuries and to provide evidence for predicting the future trends on road traffic injuries. Data on the mortality of road traffic injury including factors as gender and age in different countries, were obtained from the World Health Organization Mortality Database. Other information on GDP per capita, urbanization, motorization and education were collected from online resources of World Bank, WHO, the United Nations Population Division and other agencies. We fitted logarithmic models of road traffic injury mortality by gender and age group, including predictors of GDP per capita, urbanization, motorization and education. Sex- and age-specific forecasting models developed by WHO that including GDP per capita, education and time etc. were also fitted. Coefficient of determination(R(2)) was used to compare the performance between our modes and WHO models. 2 626 sets of data were collected from 153 countries/regions for both genders, between 1965 and 2010. The forecasting models of road traffic injury mortality based on GDP per capita, motorization, urbanization and education appeared to be statistically significant(P forecasting models that we developed seemed to be better than those developed by WHO.

  2. Optimization of Evaporative Demand Models for Seasonal Drought Forecasting

    Science.gov (United States)

    McEvoy, D.; Huntington, J. L.; Hobbins, M.

    2015-12-01

    Providing reliable seasonal drought forecasts continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) forecasts beyond weather time scales are largely unreliable, so exploring new avenues to improve seasonal drought prediction is necessary to move towards applications and decision-making based on seasonal forecasts. A recent study has shown that evaporative demand (E0) anomaly forecasts from the Climate Forecast System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly forecasts during drought events over CONUS, and E0 drought forecasts may be particularly useful during the growing season in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, forecasts of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and season one forecasts; which are

  3. Applying the Dufournaud econometric model to the determination of the prices dynamics impact over the national economy and over its main vulnerable sectors in connection with the Romanian national economy specificity

    Directory of Open Access Journals (Sweden)

    Elena Claudia Serban

    2008-09-01

    Full Text Available In economics, a model represents an abstract, formal image of a phenomenon, process or economic system. It is built by symbolically reproducing the economic theory and by getting new information regarding the behavior of the objective being investigated. In economic theory there are several integrated econometric models meant to underline the interdependency between the branches of a national economy by the public utilities services, especially those connected to energy. The present article presents the first results of our efforts of building an econometric model adapted to the specificity of the Romanian economy, that would underline the impact of the prices modification for public utilities on the Romanian economy as a hole, on the competitiveness of Romanian companies and on the inflation.

  4. Climate model forecast biases assessed with a perturbed physics ensemble

    Science.gov (United States)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2017-09-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  5. Climate model forecast biases assessed with a perturbed physics ensemble

    Science.gov (United States)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2016-10-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  6. Model for Adjustment of Aggregate Forecasts using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Taracena–Sanz L. F.

    2010-07-01

    Full Text Available This research suggests a contribution in the implementation of forecasting models. The proposed model is developed with the aim to fit the projection of demand to surroundings of firms, and this is based on three considerations that cause that in many cases the forecasts of the demand are different from reality, such as: 1 one of the problems most difficult to model in the forecasts is the uncertainty related to the information available; 2 the methods traditionally used by firms for the projection of demand mainly are based on past behavior of the market (historical demand; and 3 these methods do not consider in their analysis the factors that are influencing so that the observed behaviour occurs. Therefore, the proposed model is based on the implementation of Fuzzy Logic, integrating the main variables that affect the behavior of market demand, and which are not considered in the classical statistical methods. The model was applied to a bottling of carbonated beverages, and with the adjustment of the projection of demand a more reliable forecast was obtained.

  7. SARX Model Application for Industrial Power Demand Forecasting in Brazil

    Directory of Open Access Journals (Sweden)

    Alessandra de Ávila Montini

    2012-06-01

    Full Text Available The objective of this paper is to propose the application of the SARX model to arrive at industrial power consumption forecasts in Brazil, which are critical to support decision-making in the energy sector, based on technical, economic and environmentally sustainable grounds. The proposed model has a seasonal component and considers the influence of exogenous variables on the projection of the dependent variable and utilizes an autoregressive process for residual modeling so as to improve its explanatory power. Five exogenous variables were included: industrial capacity utilization, industrial electricity tariff, industrial real revenues, exchange rate, and machinery and equipment inflation. In addition, the model assumed that power forecast was dependent on its own time lags and also on a dummy variable to reflect 2009 economic crisis. The study used 84 monthly observations, from January 2003 to December 2009. The backward method was used to select exogenous variables, assuming a 0.10 descriptive value. The results showed an adjusted coefficient of determination of 93.9% and all the estimated coefficients were statistically significant at a 0.10 descriptive level. Forecasts were also made from January to May 2010 at a 95% confidence interval, which included actual consumption values for this period. The SARX model has demonstrated an excellent performance for industrial power consumption forecasting in Brazil.

  8. An experimental seasonal hydrological forecasting system over the Yellow River basin - Part 2: The added value from climate forecast models

    Science.gov (United States)

    Yuan, Xing

    2016-06-01

    This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed

  9. Time series modelling and forecasting of emergency department overcrowding.

    Science.gov (United States)

    Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Tahon, Christian

    2014-09-01

    Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

  10. Networking Sensor Observations, Forecast Models & Data Analysis Tools

    Science.gov (United States)

    Falke, S. R.; Roberts, G.; Sullivan, D.; Dibner, P. C.; Husar, R. B.

    2009-12-01

    This presentation explores the interaction between sensor webs and forecast models and data analysis processes within service oriented architectures (SOA). Earth observation data from surface monitors and satellite sensors and output from earth science models are increasingly available through open interfaces that adhere to web standards, such as the OGC Web Coverage Service (WCS), OGC Sensor Observation Service (SOS), OGC Web Processing Service (WPS), SOAP-Web Services Description Language (WSDL), or RESTful web services. We examine the implementation of these standards from the perspective of forecast models and analysis tools. Interoperable interfaces for model inputs, outputs, and settings are defined with the purpose of connecting them with data access services in service oriented frameworks. We review current best practices in modular modeling, such as OpenMI and ESMF/Mapl, and examine the applicability of those practices to service oriented sensor webs. In particular, we apply sensor-model-analysis interfaces within the context of wildfire smoke analysis and forecasting scenario used in the recent GEOSS Architecture Implementation Pilot. Fire locations derived from satellites and surface observations and reconciled through a US Forest Service SOAP web service are used to initialize a CALPUFF smoke forecast model. The results of the smoke forecast model are served through an OGC WCS interface that is accessed from an analysis tool that extract areas of high particulate matter concentrations and a data comparison tool that compares the forecasted smoke with Unattended Aerial System (UAS) collected imagery and satellite-derived aerosol indices. An OGC WPS that calculates population statistics based on polygon areas is used with the extract area of high particulate matter to derive information on the population expected to be impacted by smoke from the wildfires. We described the process for enabling the fire location, smoke forecast, smoke observation, and

  11. A multivariate heuristic model for fuzzy time-series forecasting.

    Science.gov (United States)

    Huarng, Kun-Huang; Yu, Tiffany Hui-Kuang; Hsu, Yu Wei

    2007-08-01

    Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.

  12. Econometrics in R: Past, Present, and Future

    Directory of Open Access Journals (Sweden)

    Achim Zeileis

    2008-07-01

    Full Text Available Recently, computational methods and software have been receiving more attention in the econometrics literature, emphasizing that they are integral components of modern econometric research. This has also promoted the development of many new econometrics software packages written in R and made available on the Comprehensive R Archive Network. This special volume on "Econometrics in R" features a selection of these recent activities that includes packages for econometric analysis of cross-section, time series and panel data. This introduction to the special volume highlights the contents of the contributions and embeds them into a brief overview of other past, present, and future projects for econometrics in R.

  13. Comparison of Conventional and ANN Models for River Flow Forecasting

    Science.gov (United States)

    Jain, A.; Ganti, R.

    2011-12-01

    Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

  14. Hybrid grey model to forecast monitoring series with seasonality

    Institute of Scientific and Technical Information of China (English)

    WANG Qi-jie; LIAO Xin-hao; ZHOU Yong-hong; ZOU Zheng-rong; ZHU Jian-jun; PENG Yue

    2005-01-01

    The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.

  15. Developing a model of forecasting information systems performance

    Directory of Open Access Journals (Sweden)

    G. N. Isaev

    2017-01-01

    Full Text Available Research aim: to develop a model to forecast the performance ofinformation systems as a mechanism for preliminary assessment of the information system effectiveness before the beginning of financing the information system project.Materials and methods: the starting material used the results of studying the parameters of the statistical structure of information system data processing defects. Methods of cluster analysis and regression analysis were applied.Results: in order to reduce financial risks, information systems customers try to make decisions on the basis of preliminary calculations on the effectiveness of future information systems. However, the assumptions on techno-economic justification of the project can only be obtained when the funding for design work is already open. Its evaluation can be done before starting the project development using a model of forecasting information system performance. The model is developed using regression analysis in the form of a multiple linear regression. The value of information system performance is the predicted variable in the regression equation. The values of data processing defects in the classes of accuracy, completeness and timeliness are the forecast variables. Measurement and evaluation of parameters of the statistical structure of defects were done through programmes of cluster analysis and regression analysis. The calculations for determining the actual and forecast values of the information system performance were conducted.Conclusion: in terms of implementing the model, a research of information systems was carried out, as well as the development of forecasting model of information system performance. The conducted experimental work showed the adequacy of the model. The model is implemented in the complex task of designing information systems in education and industry.

  16. Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre

    Science.gov (United States)

    Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip

    2013-04-01

    Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in

  17. Temperature sensitivity of a numerical pollen forecast model

    Science.gov (United States)

    Scheifinger, Helfried; Meran, Ingrid; Szabo, Barbara; Gallaun, Heinz; Natali, Stefano; Mantovani, Simone

    2016-04-01

    Allergic rhinitis has become a global health problem especially affecting children and adolescence. Timely and reliable warning before an increase of the atmospheric pollen concentration means a substantial support for physicians and allergy suffers. Recently developed numerical pollen forecast models have become means to support the pollen forecast service, which however still require refinement. One of the problem areas concerns the correct timing of the beginning and end of the flowering period of the species under consideration, which is identical with the period of possible pollen emission. Both are governed essentially by the temperature accumulated before the entry of flowering and during flowering. Phenological models are sensitive to a bias of the temperature. A mean bias of -1°C of the input temperature can shift the entry date of a phenological phase for about a week into the future. A bias of such an order of magnitude is still possible in case of numerical weather forecast models. If the assimilation of additional temperature information (e.g. ground measurements as well as satellite-retrieved air / surface temperature fields) is able to reduce such systematic temperature deviations, the precision of the timing of phenological entry dates might be enhanced. With a number of sensitivity experiments the effect of a possible temperature bias on the modelled phenology and the pollen concentration in the atmosphere is determined. The actual bias of the ECMWF IFS 2 m temperature will also be calculated and its effect on the numerical pollen forecast procedure presented.

  18. Interval forecasts of a novelty hybrid model for wind speeds

    Directory of Open Access Journals (Sweden)

    Shanshan Qin

    2015-11-01

    Full Text Available The utilization of wind energy, as a booming technology in the field of renewable energies, has been highly regarded around the world. Quantification of uncertainties associated with accurate wind speed forecasts is essential for regulating wind power generation and integration. However, it remains difficult work primarily due to the stochastic and nonlinear characteristics of wind speed series. Traditional models for wind speed forecasting mostly focus on generating certain predictive values, which cannot properly handle uncertainties. For quantifying potential uncertainties, a hybrid model constructed by the Cuckoo Search Optimization (CSO-based Back Propagation Neural Network (BPNN is proposed to establish wind speed interval forecasts (IFs by estimating the lower and upper bounds. The quality of IFs is assessed quantitatively using IFs coverage probability (IFCP and IFs normalized average width (IFNAW. Moreover, to assess the overall quality of IFs comprehensively, a tradeoff between informativeness (IFNAW and validity (IFCP of IFs is examined by coverage width-based criteria (CWC. As an applicative study, wind speeds from the Xinjiang Region in China are used to validate the proposed hybrid model. The results demonstrate that the proposed model can construct higher quality IFs for short-term wind speed forecasts.

  19. A systematic review of health manpower forecasting models.

    NARCIS (Netherlands)

    Martins-Coelho, G.; Greuningen, M. van; Barros, H.; Batenburg, R.

    2011-01-01

    Context: Health manpower planning (HMP) aims at matching health manpower (HM) supply to the population’s health requirements. To achieve this, HMP needs information on future HM supply and requirement (S&R). This is estimated by several different forecasting models (FMs). In this paper, we review

  20. Development of a forecast model for global air traffic emissions

    Energy Technology Data Exchange (ETDEWEB)

    Schaefer, Martin

    2012-07-01

    The thesis describes the methodology and results of a simulation model that quantifies fuel consumption and emissions of civil air traffic. Besides covering historical emissions, the model aims at forecasting emissions in the medium-term future. For this purpose, simulation models of aircraft and engine types are used in combination with a database of global flight movements and assumptions about traffic growth, fleet rollover and operational aspects. Results from an application of the model include emissions of scheduled air traffic for the years 2000 to 2010 as well as forecasted emissions until the year 2030. In a baseline scenario of the forecast, input assumptions (e.g. traffic growth rates) are in line with predictions by the aircraft industry. Considering the effects of advanced technologies of the short-term and medium-term future, the forecast focusses on fuel consumption and emissions of nitric oxides. Calculations for historical air traffic additionally cover emissions of carbon monoxide, unburned hydrocarbons and soot. Results are validated against reference data including studies by the International Civil Aviation Organization (ICAO) and simulation results from international research projects. (orig.)

  1. 101 Modelling and Forecasting Periodic Electric Load for a ...

    African Journals Online (AJOL)

    User

    2012-01-24

    Jan 24, 2012 ... In this work, three models are used to analyze the electric load capacity of a ..... Forecasting electricity prices for a day-ahead pool-based electric energy market. ... Control, Operation and Management, Hong Kong pgs.782–7.

  2. Short-term load forecasting based on a multi-model

    Energy Technology Data Exchange (ETDEWEB)

    Faller, C. [ETH, Zurich (Switzerland). Faculty of Electrical Engineering; Dvorakova, R.; Horacek, P. [Czech Technical University (Czech Republic). Faculty of Electrical Engineering

    2000-07-01

    Two algorithms for short-term electricity demand forecasting in the regional electricity distribution network are presented. Several approaches - feedforward neural network, adaptive modelling and fuzzy modelling - are applied to the forecast. Two different models are designed. A one hour forecasting is based on the General Regression Neural Network (GRNN) model and Principle Component Analysis. The multi-model with adaptive features and fuzzy reasoning is used for a longer-term forecast. (author)

  3. Performance assessment of models to forecast induced seismicity

    Science.gov (United States)

    Wiemer, Stefan; Karvounis, Dimitrios; Zechar, Jeremy; Király, Eszter; Kraft, Toni; Pio Rinaldi, Antonio; Catalli, Flaminia; Mignan, Arnaud

    2015-04-01

    Managing and mitigating induced seismicity during reservoir stimulation and operation is a critical prerequisite for many GeoEnergy applications. We are currently developing and validating so called 'Adaptive Traffic Light Systems' (ATLS), fully probabilistic forecast models that integrate all relevant data on the fly into a time-dependent hazard and risk model. The combined model intrinsically considers both aleatory and model-uncertainties, the robustness of the forecast is maximized by using a dynamically update ensemble weighting. At the heart of the ATLS approach are a variety of forecast models that range from purely statistical models, such as flow-controlled Epidemic Type Aftershock Sequence (ETAS) models, to models that consider various physical interaction mechanism (e.g., pore pressure changes, dynamic and static stress transfer, volumetric strain changes). The automated re-calibration of these models on the fly given data imperfection, degrees of freedom, and time-constraints is a sizable challenge, as is the validation of the models for applications outside of their calibrated range (different settings, larger magnitudes, changes in physical processes etc.). Here we present an overview of the status of the model development, calibration and validation. We also demonstrate how such systems can contribute to a quantitative risk assessment and mitigation of induced seismicity in a wide range of applications and time scales.

  4. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

    Science.gov (United States)

    Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

    2017-02-01

    Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

  5. Addressing the Challenges of Distributed Hydrologic Modeling for Operational Forecasting

    Science.gov (United States)

    Butts, M. B.; Yamagata, K.; Kobor, J.; Fontenot, E.

    2008-05-01

    Operational forecasting systems must provide reliable, accurate and timely flood forecasts for a range of catchments from small rapidly responding mountain catchments and urban areas to large, complex but more slowly responding fluvial systems. Flood forecasting systems have evolved from simple forecasting for flood mitigation to real-time decision support systems for real-time reservoir operations for water supply, navigation, hydropower, for managing environmental flows and habitat protection, cooling water and water quality forecasting. These different requirements lead to a number of challenges in applying distributed modelling in an operational context. These challenges include, the often short time available for forecasting that requires a trade-off between model complexity and accuracy on the one hand and on the other hand the need for efficient calculations to reduce the computation times. Limitations in the data available in real-time require modelling tools that can not only operate on a minimum of data but also take advantage of new data sources such as weather radar, satellite remote sensing, wireless sensors etc. Finally, models must not only accurately predict flood peaks but also forecast low flows and surface water-groundwater interactions, water quality, water temperature, optimal reservoir levels, and inundated areas. This paper shows how these challenges are being addressed in a number of case studies. The central strategy has been to develop a flexible modelling framework that can be adapted to different data sources, different levels of complexity and spatial distribution and different modelling objectives. The resulting framework allows amongst other things, optimal use of grid-based precipitation fields from weather radar and numerical weather models, direct integration of satellite remote sensing, a unique capability to treat a range of new forecasting problems such as flooding conditioned by surface water-groundwater interactions. Results

  6. An improved market penetration model for wind energy technology forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Lund, P.D. [Helsinki Univ. of Technology, Espoo (Finland). Advanced Energy Systems

    1995-12-31

    An improved market penetration model with application to wind energy forecasting is presented. In the model, a technology diffusion model and manufacturing learning curve are combined. Based on a 85% progress ratio that was found for European wind manufactures and on wind market statistics, an additional wind power capacity of ca 4 GW is needed in Europe to reach a 30 % price reduction. A full breakthrough to low-cost utility bulk power markets could be achieved at a 24 GW level. (author)

  7. Forecasting seasonal influenza with a state-space SIR model.

    Science.gov (United States)

    Osthus, Dave; Hickmann, Kyle S; Caragea, Petruţa C; Higdon, Dave; Del Valle, Sara Y

    2017-03-01

    Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.

  8. Un modello econometrico per il credito bancario alle imprese in Italia (An econometric model for bank lending to companies in Italy

    Directory of Open Access Journals (Sweden)

    Luca Casolaro

    2012-04-01

    Full Text Available Questo lavoro presenta la stima di un modello econometrico del credito bancario alle imprese in Italia per il periodo 1988-2004. I prestiti bancari sono espressi in funzione del rapporto tra investimenti fissi e margine operativo lordo (che approssima il fabbisogno di finanziamenti esterni, delle attività immobilizzate nel bilancio delle imprese (misurate dallo stock di capitale e del differenziale tra il tasso sui prestiti a breve termine e il rendimento del mercato interbancario (che approssima il costo relativo del credito bancario rispetto a forme di finanziamento alternative. I risultati empirici evidenziano nel lungo periodo un legame diretto del credito bancario con le attività immobilizzate (l’elasticità è pari a uno e con il rapporto tra investimenti fissi e margine operativo lordo (l’incremento di un punto percentuale del rapporto produce un’espansione del credito del 2%. Le stime econometriche mostrano al contrario una correlazione negativa con il differenziale tra i tassi d’interesse: un aumento di 10 punti base del differenziale tra i tassi determinerebbe, infatti, una riduzione del credito nel lungo periodo pari allo 0,7%.   This paper presents the estimation of an econometric model of bank credit to firms in Italy for the period 1988-2004. Bank loans are expressed as a function of the ratio of fixed capital formation and gross operating margin (which approximates the external financing needs of fixed assets in the financial statements of companies (as measured by the stock of capital and the difference between the interest rate on short-term loans and efficiency of the interbank market (which approximates the relative cost of bank credit compared to alternative forms of financing.The empirical results show a direct link between long-term bank credit with fixed assets (the elasticity is equal to one and the ratio of fixed capital formation and gross operating profit (increase of one percentage point of the report

  9. Evaluating non-linear models on point and interval forecasts: an application with exchange rates

    Directory of Open Access Journals (Sweden)

    Emanuela Marrocu

    2005-01-01

    Full Text Available The aim of this paper is to compare the forecasting performance of SETAR and GARCH models against a linear benchmark using historical data for the returns of the Japanese yen/US dollar exchange rate. The relative performance of the models is evaluated on point forecasts and on interval forecasts. Point forecasts evaluation over the whole forecast period indicates that the performance of the models, when distinguishable, tends to favour the linear models. However, we show that if the evaluation of point forecasts is conducted over distinct subsamples or specific regimes there is more evidence of forecasting gains, especially from the SETAR models. Moreover, when we evaluate the validity of interval forecasts, the results produce clear evidence of the superiority of the non-linear models, and tend to favour especially the GARCH models.

  10. 某医院中央空调系统运行计量经济模型%Econometric model for the center air-condition system running of a hospital

    Institute of Scientific and Technical Information of China (English)

    黎韧

    2009-01-01

    An econometric model consisting of 11 explicit formulas for analyzing the center air-condifion system running of a hospital was derived according to the principles of system engineering and system dynamic. The model was tested in terms of economic significance, statistics, econometrics and stability, and the results verify that it conforms with the econometric test. The error rate ia 3.27% for the whole modal, indicating reasonable precise and stability of the model.%根据系统工程和系统动力学原理建立了由11个联立方程式组成的某医院中央空调系统运行计量经济模型,从经济意义、统计检验、计量经济学和模型稳定性4个方面对模型进行检验,检验结果表明该模型满足计量经济学检验,整个模型的误差率为3.27%,说明模型的精确度较高,模型运行较稳定.

  11. Assessment of Quantitative Precipitation Forecasts from Operational NWP Models (Invited)

    Science.gov (United States)

    Sapiano, M. R.

    2010-12-01

    Previous work has shown that satellite and numerical model estimates of precipitation have complimentary strengths, with satellites having greater skill at detecting convective precipitation events and model estimates having greater skill at detecting stratiform precipitation. This is due in part to the challenges associated with retrieving stratiform precipitation from satellites and the difficulty in resolving sub-grid scale processes in models. These complimentary strengths can be exploited to obtain new merged satellite/model datasets, and several such datasets have been constructed using reanalysis data. Whilst reanalysis data are stable in a climate sense, they also have relatively coarse resolution compared to the satellite estimates (many of which are now commonly available at quarter degree resolution) and they necessarily use fixed forecast systems that are not state-of-the-art. An alternative to reanalysis data is to use Operational Numerical Weather Prediction (NWP) model estimates, which routinely produce precipitation with higher resolution and using the most modern techniques. Such estimates have not been combined with satellite precipitation and their relative skill has not been sufficiently assessed beyond model validation. The aim of this work is to assess the information content of the models relative to satellite estimates with the goal of improving techniques for merging these data types. To that end, several operational NWP precipitation forecasts have been compared to satellite and in situ data and their relative skill in forecasting precipitation has been assessed. In particular, the relationship between precipitation forecast skill and other model variables will be explored to see if these other model variables can be used to estimate the skill of the model at a particular time. Such relationships would be provide a basis for determining weights and errors of any merged products.

  12. Nonlinear Dynamical Modeling and Forecast of ENSO Variability

    Science.gov (United States)

    Feigin, Alexander; Mukhin, Dmitry; Gavrilov, Andrey; Seleznev, Aleksey; Loskutov, Evgeny

    2017-04-01

    New methodology of empirical modeling and forecast of nonlinear dynamical system variability [1] is applied to study of ENSO climate system. The methodology is based on two approaches: (i) nonlinear decomposition of data [2], that provides low-dimensional embedding for further modeling, and (ii) construction of empirical model in the form of low dimensional random dynamical ("stochastic") system [3]. Three monthly data sets are used for ENSO modeling and forecast: global sea surface temperature anomalies, troposphere zonal wind speed, and thermocline depth; all data sets are limited by 30 S, 30 N and have horizontal resolution 10x10 . We compare results of optimal data decomposition as well as prognostic skill of the constructed models for different combinations of involved data sets. We also present comparative analysis of ENSO indices forecasts fulfilled by our models and by IRI/CPC ENSO Predictions Plume. [1] A. Gavrilov, D. Mukhin, E. Loskutov, A. Feigin, 2016: Construction of Optimally Reduced Empirical Model by Spatially Distributed Climate Data. 2016 AGU Fall Meeting, Abstract NG31A-1824. [2] D. Mukhin, A. Gavrilov, E. Loskutov , A.Feigin, J.Kurths, 2015: Principal nonlinear dynamical modes of climate variability, Scientific Reports, rep. 5, 15510; doi: 10.1038/srep15510. [3] Ya. Molkov, D. Mukhin, E. Loskutov, A. Feigin, 2012: Random dynamical models from time series. Phys. Rev. E, Vol. 85, n.3.

  13. Validating induced seismicity forecast models - Induced Seismicity Test Bench

    CERN Document Server

    Kiraly-Proag, Eszter; Gischig, Valentin; Wiemer, Stefan; Karvounis, Dimitrios; Doetsch, Joseph

    2016-01-01

    Induced earthquakes often accompany fluid injection, and the seismic hazard they pose threatens various underground engineering projects. Models to monitor and control induced seismic hazard with traffic light systems should be probabilistic, forward-looking, and updated as new data arrive. In this study, we propose an Induced Seismicity Test Bench to test and rank such models; this test bench can be used for model development, model selection, and ensemble model building. We apply the test bench to data from the Basel 2006 and Soultz-sous-For\\^ets 2004 geothermal stimulation projects, and we assess forecasts from two models: Shapiro and Smoothed Seismicity (SaSS) and Hydraulics and Seismics (HySei). These models incorporate a different mix of physics-based elements and stochastic representation of the induced sequences. Our results show that neither model is fully superior to the other. Generally, HySei forecasts the seismicity rate better after shut-in, but is only mediocre at forecasting the spatial distri...

  14. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    Energy Technology Data Exchange (ETDEWEB)

    Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

    2011-03-28

    Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

  15. Forecasting wind-driven wildfires using an inverse modelling approach

    Directory of Open Access Journals (Sweden)

    O. Rios

    2014-06-01

    Full Text Available A technology able to rapidly forecast wildfire dynamics would lead to a paradigm shift in the response to emergencies, providing the Fire Service with essential information about the ongoing fire. This paper presents and explores a novel methodology to forecast wildfire dynamics in wind-driven conditions, using real-time data assimilation and inverse modelling. The forecasting algorithm combines Rothermel's rate of spread theory with a perimeter expansion model based on Huygens principle and solves the optimisation problem with a tangent linear approach and forward automatic differentiation. Its potential is investigated using synthetic data and evaluated in different wildfire scenarios. The results show the capacity of the method to quickly predict the location of the fire front with a positive lead time (ahead of the event in the order of 10 min for a spatial scale of 100 m. The greatest strengths of our method are lightness, speed and flexibility. We specifically tailor the forecast to be efficient and computationally cheap so it can be used in mobile systems for field deployment and operativeness. Thus, we put emphasis on producing a positive lead time and the means to maximise it.

  16. Improving the reliability of seasonal climate forecasts through empirical downscaling and multi-model considerations; presentation

    CSIR Research Space (South Africa)

    Landman, WA

    2012-11-01

    Full Text Available -forecasts) have been generated by a statistical model and by state-of-the-art fully coupled ocean-atmosphere general circulation models. Since forecast users generally require well-calibrated probability forecasts we employ a model output statistics approach...

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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…

  20. Towards operational modeling and forecasting of the Iberian shelves ecosystem.

    Directory of Open Access Journals (Sweden)

    Martinho Marta-Almeida

    Full Text Available There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a Nutrients-Phytoplankton-Zooplankton-Detritus biogeochemical module (NPZD. In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmol N m(-3. Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill.

  1. operational modelling and forecasting of the Iberian shelves ecosystem

    Science.gov (United States)

    Marta-Almeida, M.; Reboreda, R.; Rocha, C.; Dubert, J.; Nolasco, R.; Cordeiro, N.; Luna, T.; Rocha, A.; Silva, J. Lencart e.; Queiroga, H.; Peliz, A.; Ruiz-Villarreal, M.

    2012-04-01

    There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a NPZD biogeochemical module. In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmolN m-3). Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill.

  2. A Novel Fuzzy Document Based Information Retrieval Model for Forecasting

    Directory of Open Access Journals (Sweden)

    Partha Roy

    2017-06-01

    Full Text Available Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1 In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2 A unique approach is followed while modeling the information retrieval (IR system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index of National Stock Exchange of India (NSE is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.

  3. Application of Improved Grey Prediction Model to Petroleum Cost Forecasting

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The grey theory is a multidisciplinary and generic theory that deals with systems that lack adequate information and/or have only poor information. In this paper, an improved grey model using step function was proposed.Petroleum cost forecast of the Henan oil field was used as the case study to test the efficiency and accuracy of the proposed method. According to the experimental results, the proposed method obviously could improve the prediction accuracy of the original grey model.

  4. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    Science.gov (United States)

    Wanders, Niko; Wood, Eric F.

    2016-09-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982-2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a ‘standard’ equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.

  5. Gray comprehensive assessment and optimal selection of water consumption forecasting model

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A comprehensive assessing method based on the principle of the gray system theory and gray relational grade analysis was put forward to optimize water consumption forecasting models. The method provides a better accuracy for the assessment and the optimal selection of the water consumption forecasting models. The results show that the forecasting model built on this comprehensive assessing method presents better self-adaptability and accuracy in forecasting.

  6. Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model

    Directory of Open Access Journals (Sweden)

    Xia Li

    2014-01-01

    Full Text Available Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.

  7. Forecasting TRY/USD Exchange Rate with Various Artificial Neural Network Models

    Directory of Open Access Journals (Sweden)

    Cagatay Bal

    2017-02-01

    Full Text Available Exchange rate forecasting is one of the most common subjects among the forecasting problem field. Researchers and academicians from many different disciplines proposed various approaches for better exchange rate forecasting. In recent years, for solving the stated forecasting problem artificial neural networks have become successful tool to obtain solutions. Many different artificial neural networks have been used, developed and still developing for even better and trustable forecasts. In this study, TRY/USD exchange rate forecasting is modeled with different learning algorithms, activations functions and performance measures. Various Artificial Neural Network (ANN models for better forecasting were investigated, compared and the obtained forecasting results interpreted respectively. The results of the application show that Variable Learning Rate Backpropagation learning algorithm with tan-sigmoid activation function has the best performance for TRY/USD exchange rate forecasting.

  8. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach

    Science.gov (United States)

    Zhao, Tongtiegang; Schepen, Andrew; Wang, Q. J.

    2016-10-01

    The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP's capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982-2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.

  9. Modelling and forecasting monthly swordfish catches in the Eastern Mediterranean

    Directory of Open Access Journals (Sweden)

    Konstantinos I. Stergiou

    2003-04-01

    Full Text Available In this study, we used the X-11 census technique for modelling and forecasting the monthly swordfish (Xiphias gladius catches in the Greek Seas during 1982-1996 and 1997 respectively, using catches reported by the National Statistical Service of Greece (NSSG. Forecasts built with X-11 were also compared with those derived from ARIMA andWinter’s exponential smoothing (WES models. The X-11 method captured the features of the study series and outperformed the other two methods, in terms of both fitting and forecasting performance, for all the accuracy measures used. Thus, with the exception of October, November and December 1997, when the corresponding absolute percentage error(APE values were very high (as high as 178.6% because of the low level of the catches, monthly catches during the remaining months of 1997 were predicted accurately, with a mean APE of 12.5%. In contrast, the mean APE values of the other two methods for the same months were higher (ARIMA: 14.6%; WES: 16.6%. The overall good performance of X-11 andthe fact that it provides an insight into the various components (i.e. the seasonal, trend-cycle and irregular components of the time series of interest justify its use in fisheries research. The basic features of the swordfish catches revealed by the application of the X-11 method, the effect of the length of the forecasting horizon on forecasting accuracy and the accuracy of the catches reported by NSSG are also discussed.

  10. Crop Yield Forecasted Model Based on Time Series Techniques

    Institute of Scientific and Technical Information of China (English)

    Li Hong-ying; Hou Yan-lin; Zhou Yong-juan; Zhao Hui-ming

    2012-01-01

    Traditional studies on potential yield mainly referred to attainable yield: the maximum yield which could be reached by a crop in a given environment. The new concept of crop yield under average climate conditions was defined in this paper, which was affected by advancement of science and technology. Based on the new concept of crop yield, the time series techniques relying on past yield data was employed to set up a forecasting model. The model was tested by using average grain yields of Liaoning Province in China from 1949 to 2005. The testing combined dynamic n-choosing and micro tendency rectification, and an average forecasting error was 1.24%. In the trend line of yield change, and then a yield turning point might occur, in which case the inflexion model was used to solve the problem of yield turn point.

  11. Developing energy forecasting model using hybrid artificial intelligence method

    Institute of Scientific and Technical Information of China (English)

    Shahram Mollaiy-Berneti

    2015-01-01

    An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

  12. Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    In this paper we consider the forecasting performance of a well-defined class of flexible models, the so-called single hidden-layer feedforward neural network models. A major aim of our study is to find out whether they, due to their flexibility, are as useful tools in economic forecasting as some...... previous studies have indicated. When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. In fact, their parameters are not even globally...... on the linearisation idea: the Marginal Bridge Estimator and Autometrics. Second, one must decide whether forecasting should be carried out recursively or directly. Comparisons of these two methodss exist for linear models and here these comparisons are extended to neural networks. Finally, a nonlinear model...

  13. Uncertainty calculation in transport models and forecasts

    DEFF Research Database (Denmark)

    Manzo, Stefano; Prato, Carlo Giacomo

    in a four-stage transport model related to different variable distributions (to be used in a Monte Carlo simulation procedure), assignment procedures and levels of congestion, at both the link and the network level. The analysis used as case study the Næstved model, referring to the Danish town of Næstved2...... the uncertainty propagation pattern over time specific for key model outputs becomes strategically important. 1 Manzo, S., Nielsen, O. A. & Prato, C. G. (2014). The Effects of uncertainty in speed-flow curve parameters on a large-scale model. Transportation Research Record, 1, 30-37. 2 Manzo, S., Nielsen, O. A...

  14. Gold versus stock investment: An econometric analysis

    Directory of Open Access Journals (Sweden)

    Martin Surya Mulyadi

    2012-06-01

    Full Text Available It is important to have a portfolio in investment to diversify the investment to different kinds of instruments. Based on previous research, it is concluded that gold is a good portfolio diversifier, a hedge against stock and safe haven in extreme stock market condition. As an investment instrument, stock is exposed to macroeconomic risks and global stock market risks. In this research, we conduct a comparison between the stock investment and gold investment by using the probit econometric model and data from 1997 to 2011. The final result obtained from the model shows that the gold investment is more advantageous than the stock investment.

  15. 交通事故宏观计量经济学模型(英文)%Macroscopic econometrics model of traffic accident

    Institute of Scientific and Technical Information of China (English)

    王军雷; 孙小端; 贺玉龙; 侯树展

    2012-01-01

    From the points of macroscopic factors such as economic development level,population number,vehicle ownership and road condition,the regularities of traffic accidents at home and abroad were analyzed.The relations among per capita GDP and vehicle ownership per 1 000 population,mortality per 10 000 vehicles,mortality per 100 000 population were studied.Based on macroscopic econometrics and Cobb-Douglas function,the panel data model of traffic accident was set up by using the historical data in seven countries.Fixed effect model and random effect model were used to estimate parameters respectively,Hausman test was carried out,and the macroscopic econometrics models of traffic accidents for the seven countries were set up.Calculation result shows that among the parameters of traffic accidents,mortality per 100 000 population is negative correlation with per capita GDP and per capita road length,mortality per 100 000 population is positive correlation with vehicle ownership per 1 000 population.Through Hausman test,chi-square distribution value is 3.91 when freedom is 3,the probability is 0.02 and less than the confidence level which is 0.05.Compared with the random effect model,all the confidence levels of variables for fixed effect model are less than 0.05,and the goodness of fit is better.So the fixed effect model is effective.3 tabs,6 figs,18 refs.%从经济发展水平、人口数量、汽车保有量、道路情况等宏观因素入手,研究了国内外道路交通事故规律,分析了人均GDP与千人汽车保有量、万车死亡率、10万人口死亡率之间的关系。以宏观计量经济学和柯布-道格拉斯函数为基础,利用7个国家的历史数据构建了交通事故面板数据模型。分别采用固定效应模型和随机效应模型进行参数估计,并进行了Hausman检验,得到7个国家的交通事故宏观计量经济学模型。计算结果表明:在交通事故参数中,10万人口死亡率与人均GDP、人均道路长度

  16. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Hawaiian islands of Oahu,...

  17. Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

    DEFF Research Database (Denmark)

    Quinonero, Joaquin; Girard, Agathe; Larsen, Jan

    2003-01-01

    The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaus....... The capability of the method is demonstrated for forecasting of time-series and compared to approximate methods.......The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models...... such as the Gaussian process and the relevance vector machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting...

  18. On Comparing NWP and Radar Nowcast Models for Forecasting of Urban Runoff

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Bøvith, T.; Rasmussen, Michael R.;

    2012-01-01

    The paper compares quantitative precipitation forecasts using weather radars and numerical weather prediction models. In order to test forecasts under different conditions, point-comparisons with quantitative radar precipitation estimates and raingauges are presented. Furthermore, spatial...

  19. Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model 7-day hourly forecast for the region surrounding the Main Hawaiian Islands (MHI)...

  20. Recent advances in operational seasonal forecasting in South Africa: Models, infrastructure and networks

    CSIR Research Space (South Africa)

    Landman, WA

    2011-11-01

    Full Text Available The various institutions involved with seasonal forecast development and production are discussed. New modelling approaches and the establishment of infrastructures to improve forecast dissemination are discussed....