Econometric models for forecasting of macroeconomic indices
Sukhanova, E. I.; Shirnaeva, S. Y.; Mokronosov, A. 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 development in the past and their cause and effect interrelations. The aim of the article is to build econometric models for macroeconomic indices forec...
Econometric Models for Forecasting of Macroeconomic Indices
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…
Forecasting Inflation through Econometrics Models: An Empirical ...
African Journals Online (AJOL)
This article aims at modeling and forecasting inflation in Pakistan. For this purpose a number of econometric approaches are implemented and their results are compared. In ARIMA models, adding additional lags for p and/or q necessarily reduced the sum of squares of the estimated residuals. When a model is estimated ...
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.
FORECASTING BY ECONOMETRIC MODELS AS SUPPORT TO MANAGEMENT
TINDE DOBRODOLAC
2011-01-01
In the contemporary environment characterized by the dynamic structure of factors and the unpredictability of the relations existing between them, the central problem is the selection of strategic goals. Forecasting is the necessary precursor to the planning process and includes research into the future course of events. Numerous methods and techniques of forecasting are used nowadays. Econometric models can be used successfully for predicting the future development of a phenomenon, and there...
A national econometric forecasting model of the dental sector.
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, the proportion of the population covered by private dental insurance, the cost of hiring clericals and dental assistants, and relevant government policies. In a test of its reliability, the model forecast dental sector behavior quite accurately for the period 1971 through 1977. PMID:7461974
Econometric Forecasting Models for Air Traffic Passenger of Indonesia
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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.
Malugin, Vladimir; Demidenko , Mikhail; Kalechits, Dmitry; Miksjuk , Alexei; Tsukarev , Taras
2009-01-01
A system of econometric models designed for forecasting target monetary indicators as well as conducting monetary policy scenarios analysis is presented. The econometric models integrated in the system are represented in the error correction form and are interlinked by means of monetary policy instruments variables, common exogenous variables characterizing external shocks, and monetary policy target endogenous variables. Forecast accuracy estimates and monetary policy analysis results are pr...
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.
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...
DEFF Research Database (Denmark)
Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric
2004-01-01
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.......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...
DEFF Research Database (Denmark)
Nielsen, P.; Jiang, L. P.; Rytter, N. G. M.
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 is pr...
Econometric models for biohydrogen development.
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. Copyright © 2011 Elsevier Ltd. All rights reserved.
Forecasting space weather: Can new econometric methods improve accuracy?
Reikard, Gordon
2011-06-01
Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the A p geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the A p index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data.
Econometric modelling of conservation
International Nuclear Information System (INIS)
Parker, J.C.; Seal, D.J.
1990-01-01
The issue of energy conservation in general, and conservation in the natural gas markets in particular, has recently had a much lower profile than in the past, when energy prices were significantly higher and energy costs composed a much larger proportion of industrial operating costs than today. The recent downward trend in energy prices has diverted attention away from this issue. In the face of expected significant real price increases, increasing pressure from environmental groups, and directives on the part of regulator authorities, conservation is once again becoming a topic of consideration in the energy industry. From the point of view of gas demand forecasting, conservation has received too little attention. The intentions of this paper are to establish the need for forecasting conservation in the natural gas utility sector, and to construct a model of industrial demand which incorporates conservation and is appropriate for use as a forecasting tool
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.
Crystal study and econometric model
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.
Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator
Fernández-Vázquez, Esteban; Moreno, Blanca
2017-10-01
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.
Econometric Model – A Tool in Financial Management
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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.
Descriptive and Critical Review of Multiregional Econometric Models of the United States
Herman, Amy L.
1984-01-01
Many methodological tools exist for use by regional planners and policy analysts concerned with the implications of federal and regional policies for regional economies. Regional and multire gional econometric models represent a class of such tools not widely available for public use outside their institutional settings. The purpose of such models is to provide economic forecasting and policy simulations with analytic flexibility. Accordingly, the methods employed in developing econometric m...
International Nuclear Information System (INIS)
Henry, C.
1988-01-01
The 1988 progress report of the Econometrics laboratory (Polytechnic School, France), is presented. Microeconomy is the main research field of the Center. The applications involve the market organization, the national development, the natural resources and the education. One of the research topics is the development of theorems which allow a relationship between economic equilibrium and optimization, for the increasing efficiency economies. Concerning industrial economy, the investigations are also based on the in situ studies and on the socio-economic factors. The research projects include land selling transactions and public regulation activities, the environment management and the obtention of educational means. The published papers, the congress communications and the thesis, are listed [fr
Spatial Econometric data analysis: moving beyond traditional models
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
Econometric Evaluation of Asset Pricing Models
Lars Peter Hansen; John Heaton; Erzo Luttmer
1993-01-01
In this article we provide econometric tools for the evaluation of intertemporal asset pricing models using specification-error and volatility bounds. We formulate analog estimators of these bounds, give conditions for consistency, and derive the limiting distribution of these estimators. The analysis incorporates market frictions such as short-sale constraints and proportional transactions costs. Among several applications we show how to use the methods to assess specific asset pricing model...
Technology trends in econometric energy models: Ignorance or information?
International Nuclear Information System (INIS)
Boyd, G.; Kokkelenberg, E.; State Univ., of New York, Binghamton, NY; Ross, M.; Michigan Univ., Ann Arbor, MI
1991-01-01
Simple time trend variables in factor demand models can be statistically powerful variables, but may tell the researcher very little. Even more complex specification of technical change, e.g. factor biased, are still the economentrician's ''measure of ignorance'' about the shifts that occur in the underlying production process. Furthermore, in periods of rapid technology change the parameters based on time trends may be too large for long run forecasting. When there is clearly identifiable engineering information about new technology adoption that changes the factor input mix, data for the technology adoption may be included in the traditional factor demand model to economically model specific factor biased technical change and econometrically test their contribution. The adoption of thermomechanical pulping (TMP) and electric are furnaces (EAF) are two electricity intensive technology trends in the Paper and Steel industries, respectively. This paper presents the results of including these variables in a tradition econometric factor demand model, which is based on the Generalized Leontief. The coefficients obtained for this ''engineering based'' technical change compares quite favorably to engineering estimates of the impact of TMP and EAF on electricity intensities, improves the estimates of the other price coefficients, and yields a more believable long run electricity forecast. 6 refs., 1 fig
Econometric models for predicting confusion crop ratios
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.
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.
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...
Directory of Open Access Journals (Sweden)
Constantin ANGHELACHE
2017-06-01
Full Text Available Gross Domestic Product is the most representative synthetic indicator that expresses the evolution of the national economy. This macroeconomic indicator is used in the analysis of the level of the national economy, as well as the dynamic evolution of the national economy. In the forecast studies we rely on GDP evolution. In these situations, we might identify the factors of economic growth, and their influence. On the evolution of GDP have influence some factors: employees, labour productivity, the level of technology, investments and foreign direct investment, imports, exports or net exports, total consumption, and so on. We can analyze the data series and graphical representation. Detailed analysis is performed using econometric methods, parameters which express interdependence, meaning and intensity of correlation. Thus, we estimate the economic developments. The authors studied and proposed some econometric models for the analysis of economic growth/forecast. The novelty is that we adapt some econometric models to macroeconomic analysis.
Tourism Demand Modelling and Forecasting: A Review of Recent Research
Song, H; Li, G
2008-01-01
This paper reviews the published studies on tourism demand modelling and forecasting since 2000. One of the key findings of this review is that the methods used in analysing and forecasting the demand for tourism have been more diverse than those identified by other review articles. In addition to the most popular time-series and econometric models, a number of new techniques have emerged in the literature. However, as far as the forecasting accuracy is concerned, the study shows that there i...
Forecasting labour supply and population: An integrated stochastic model
Fuchs, Johann; Söhnlein, Doris; Weber, Brigitte; Weber, Enzo
2017-01-01
This paper presents a stochastic integrated model to forecast the German population and labour supply until 2060. Within a cohort-component approach, the population forecast applies principal components to birth, mortality, emigration and immigration rates. The labour force participation rates are estimated by means of an econometric time series approach. All time series are forecast by bootstrapping. This allows fully integrated simulations and the possibility to illustrate the uncertainties...
An econometric model of the hardwood lumber market
William G. Luppold
1982-01-01
A recursive econometric model with causal flow originating from the demand relationship is used to analyze the effects of exogenous variables on quantity and price of hardwood lumber. Wage rates, interest rates, stumpage price, lumber exports, and price of lumber demanders' output were the major factors influencing quantities demanded and supplied and hardwood...
An econometric model of the U.S. pallet market
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...
National Research Council Canada - National Science Library
Hojnowski, Ronald A
2005-01-01
...) and assigned to Navy Regional recruiting commands. Through use of an econometric goaling and forecasting model employed by CNRC and a less complicated weighting system used by Regions, goal shares are generated using factors believed to accurately...
Deriving dynamic marketing effectiveness from econometric time series models
Horváth, C.; Franses, Ph.H.B.F.
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 literature are unit roots, cointegration, structural breaks and impulse response functions. In this paper we summarize the most important concepts by reviewing all possible empirical cases that can...
Econometric model for age- and population-dependent radiation exposures
International Nuclear Information System (INIS)
Sandquist, G.M.; Slaughter, D.M.; Rogers, V.C.
1991-01-01
The economic impact associated with ionizing radiation exposures in a given human population depends on numerous factors including the individual's mean economic status as a function age, the age distribution of the population, the future life expectancy at each age, and the latency period for the occurrence of radiation-induced health effects. A simple mathematical model has been developed that provides an analytical methodology for estimating the societal econometrics associated with radiation effects are to be assessed and compared for economic evaluation
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.
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,...
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...
Empirical spatial econometric modelling of small scale neighbourhood
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.
Econometric modeling of electricity consumption in post-war Lebanon
International Nuclear Information System (INIS)
Nasr, G.E.; Badr, E.A.; Dibeh, G.
2000-01-01
This paper applies econometric models to investigate determinants of electrical energy consumption in post-war Lebanon. The impact of the Gross Domestic Product (GDP), proxied by total imports (TI), and degree days (DD) on electricity consumption is investigated over different time spans covering the period from 1993 to 1997. The time spans are chosen according to the rationing level of electricity supply. For the 1993-1994 time span, TI is found to be a significant determinant of energy consumption, whereas, DD has a negative correlation. This inconsistency might be attributed to an extensive rationing policy followed during this period. For the 1995-1997 time span which includes reduced rationing period (1995), all electrical energy consumption determinants are found to be significant at the 5% significance level. Analysis results for the rationing free 1996-1997 time span also show the significance of TI and DD at the 5% level. Furthermore, cointegration analysis for the 1995-1997 and 1996-1997 subsets reveals the existence of a long-run relationship between all variables. In addition, error correction models for both subsets are developed to predict short-run dynamics. Finally, statistical performance measures such as mean square error, mean average deviation and mean average percentage error are presented for all models
Econometric Model of Rice Policy Based On Presidential Instruction
Abadi Sembiring, Surya; Hutauruk, Julia
2018-01-01
The objective of research is to build an econometric model based on Presidential Instruction rice policy. The data was monthly time series from March 2005 to September 2009. Rice policy model specification using simultaneous equation, consisting of 14 structural equations and four identity equation, which was estimated using Two Stages Least Squares (2SLS) method. The results show that: (1) an increase of government purchasing price of dried harvest paddy has a positive impact on to increase in total rice production and community rice stock, (2) an increase community rice stock lead to decrease the rice imports, (3) an increase of the realization of the distribution of subsidized ZA fertilizers and the realization of the distribution of subsidized NPK fertilizers has a positive impact on to increase in total rice production and community rice stock and to reduce rice imports, (4) the price of the dried harvest paddy is highly responsive to the water content of dried harvest paddy both the short run and long run, (5) the quantity of rice imported is highly responsive to the imported rice price, both short run and long run.
Econometric methods for energy planning and policy
International Nuclear Information System (INIS)
Bhatia, R.
1989-01-01
The paper reports on the following: econometric models are often used in energy planning and policy for energy demand analysis at the macro and sectorial levels; estimating income and price elasticities of demand which can be used to analyze effects of growth and price changes; assessing interfuel and interfactor substitutions; forecasting energy demand; and estimating cost functions and forecasting supply. The illustrations in the paper are confined to single equation systems estimated by least squares method as used in analyzing changes in aggregate energy demand and sectorial energy demand. The use of econometric methods is illustrated with the help of empirical studies from a few countries (notably India). 2 tabs
Battlescale Forecast Model Sensitivity Study
National Research Council Canada - National Science Library
Sauter, Barbara
2003-01-01
.... Changes to the surface observations used in the Battlescale Forecast Model initialization led to no significant changes in the resulting forecast values of temperature, relative humidity, wind speed, or wind direction...
Development of an expert system in econometrics. Application to energy demand modelling
International Nuclear Information System (INIS)
Fauveau, A.
1993-01-01
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)
An econometric simulation model of income and electricity demand in Alaska's Railbelt, 1982-2022
Energy Technology Data Exchange (ETDEWEB)
Maddigan, R.J.; Hill, L.J.; Hamblin, D.M.; Van Dyke, J.W.; Brown, T.C.
1987-01-01
This report describes the specification of-and forecasts derived from-the Alaska Railbelt Electricity Load, Macroeconomic (ARELM) model. ARELM was developed as an independent, modeling tool for the evaluation of the need for power from the Susitna Hydroelectric Project which has been proposed by the Alaska Power Authority. ARELM is an econometric simulation model consisting of 61 equations - 46 behavioral equations and 15 identities. The system includes two components: (1) ARELM-MACRO which is a system of equations that simulates the performance of both the total Alaskan and Railbelt macroeconomies and (2) ARELM-LOAD which projects electricity-related activity in the Alaskan Railbelt region. The modeling system is block recursive in the sense that forecasts of population, personal income, and employment in the Railbelt derived from ARELM-MACRO are used as explanatory variables in ARELM-LOAD to simulate electricity demand, the real average price of electricity, and the number of customers in the Railbelt. Three scenarios based on assumptions about the future price of crude oil are simulated and documented in the report. The simulations, which do not include the cost-of-power impacts of Susitna-based generation, show that the growth rate in Railbelt electricity load is between 2.5 and 2.7% over the 1982 to 2022 forecast period. The forecasting results are consistent with other projections of load growth in the region using different modeling approaches.
Econometric models of power prices. An approach to market monitoring in the Western US
International Nuclear Information System (INIS)
Barmack, Matthew; Kahn, Edward; Tierney, Susan; Goldman, Charles
2008-01-01
Given the limitations of data and resources available for market monitoring in electricity markets where regional transmission organizations (RTO) do not exist, we argue that econometric models of power prices could provide a useful screening tool for market monitoring. To explore its feasibility, we developed several econometric models of power prices at two major trading hubs in the West: Palo Verde and Mid-Columbia. We show that our models explain a large portion of the variation in power prices in Palo Verde and can establish a benchmark that can be used to identify outlier prices that are potentially the result of anti-competitive behavior. (author)
Potential of Wolfram technologies in construction and research of econometric models
Directory of Open Access Journals (Sweden)
Dmitry A. Vlasov
2017-12-01
Full Text Available In the center of attention of article didactic, applied and research potentials of technologies of the modern knowledge base and a set of computing algorithms Wolfram in creation and a research of econometric models. Econometric models and methods traditionally play a special role in applied mathematical training of students of an economic bachelor degree in Plekhanov Russian University of Economics. Within this article experience of forming of content of applied mathematical training of future bachelor of economy and methodical features of use of information technologies in the course of econometric modeling of social and economic situations and teaching subject matters of «The econometrician (basic level» for students of an economic bachelor degree and «The econometrician (advanced level» for students of an economic magistracy is provided. The allocated sixteen tools fully allow to focus attention to development of innovative components of professional competence of future bachelors of economy.
International Nuclear Information System (INIS)
Honkapuro, S.; Lassila, J.; Viljainen, S.; Tahvanainen, K.; Partanen, J.
2004-01-01
Electricity distribution companies operate in the state of natural monopolies since building of parallel networks is not cost-effective. Monopoly companies do not have pressure from the open markets to keep their prices and costs at reasonable level. The regulation of these companies is needed to prevent the misuse of the monopoly position. Regulation is usually focused either on the profit of company or on the price of electricity. In this document, the usability of an econometric model in the regulation of electricity distribution companies is evaluated. Regulation method which determines allowed income for each company with generic computation model can be seen as an econometric model. As the special case of an econometric model, the method called Network Performance Assessment Model, NPAM (Naetnyttomodellen in Swedish), is analysed. NPAM is developed by Swedish Energy Agency (STEM) for the regulation of electricity distribution companies. Both theoretical analysis and calculations of an example network area are presented in this document to find the major directing effects of the model. The parameters of NPAM, which are used in the calculations of this research report, were dated on 30th of March 2004. These parameters were most recent available at the time when analysis was done. However, since NPAM is under development, the parameters have been constantly changing. Therefore slightly changes in the results can occur if calculations were made with latest parameters. However, main conclusions are same and do not depend on exact parameters. (orig.)
International Nuclear Information System (INIS)
Honkapuro, S.; Lassila, J.; Viljainen, S.; Tahvanainen, K.; Partanen, J.
2004-01-01
Electricity distribution companies operate in the state of natural monopolies since building of parallel networks is not cost- effective. Monopoly companies do not have pressure from the open markets to keep their prices and costs at reasonable level. The regulation of these companies is needed to prevent the misuse of the monopoly position. Regulation is usually focused either on the profit of company or on the price of electricity. Regulation method which determines allowed income for each company with generic computation model can be seen as an econometric model. In this document, the usability of an econometric model in the regulation of electricity distribution companies is evaluated. As the special case of an econometric model, the method called Network Performance Assessment Model, NPAM (Naetnyttomodellen in Swedish), is analysed. NPAM is developed by Swedish Energy Agency (STEM) for the regulation of electricity distribution companies. Both theoretical analysis and calculations of an example network area are presented in this document to find the major directing effects of the model. The parameters of NPAM, which are used in the calculations of this research report, were dated on 30th of March 2004. These parameters were most recent ones available at the time when analysis was done. However, since NPAM have been under development, the parameters have been constantly changing. Therefore slight changes might occur in the numerical results of calculations if they were made with the latest set of parameters. However, main conclusions are same and do not depend on exact parameters
Modeling and forecasting natural gas demand in Bangladesh
International Nuclear Information System (INIS)
Wadud, Zia; Dey, Himadri S.; Kabir, Md. Ashfanoor; Khan, Shahidul I.
2011-01-01
Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity - around 1.5 - for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country. - Highlights: → Natural gas demand is modeled using dynamic econometric methods, first of its kind in Bangladesh. → Income elasticity for aggregate natural gas demand in Bangladesh is large-around 1.5. → Demand is price insensitive, indicating too low prices and/or presence of large suppressed demand. → Demand forecasts reveal large divergence from previous estimates, which is important for planning. → Attempts to model demand for end-use sectors were successful only for the industrial sector.
Directory of Open Access Journals (Sweden)
Pilko Andriy D.
2017-07-01
Full Text Available The publication is aimed at coverage of the results of a study on existing approaches to the setting and solving the task of evaluation and analysis of efficiency of management of production processes at forestry enterprises, as well as implementation and development (in line with the industry specificity of the previously proposed approach to evaluating, analyzing and forecasting the efficiency of management of the production process by means of development and application of the economic-mathematical modeling capabilities. A study on the efficiency of the production process management and the usage of enterprise’s basic production assets has been conducted with application of discriminantal and simultative econometric models. Further development of the proposed approach could provide an additional methodical basis for planning activities to improve the management of production at forestry enterprises.
Essays on financial econometrics : modeling the term structure of interest rates
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.
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.
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.
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.
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...
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...
Local Government Budgeting: The Econometric Comparison of Political and Bureaucratic Models.
Feldstein, Martin; Frisch, Daniel
An interesting econometric problem is to decide whether any given state in the budget process is an example of the political or the bureaucratic model of budgeting. The current paper presents a method of deciding this question and then uses it to study local governmental spending on education. The method is based on the important difference…
Forecasting with Dynamic Regression Models
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.
Parameterized examination in econometrics
Malinova, Anna; Kyurkchiev, Vesselin; Spasov, Georgi
2018-01-01
The paper presents a parameterization of basic types of exam questions in Econometrics. This algorithm is used to automate and facilitate the process of examination, assessment and self-preparation of a large number of students. The proposed parameterization of testing questions reduces the time required to author tests and course assignments. It enables tutors to generate a large number of different but equivalent dynamic questions (with dynamic answers) on a certain topic, which are automatically assessed. The presented methods are implemented in DisPeL (Distributed Platform for e-Learning) and provide questions in the areas of filtering and smoothing of time-series data, forecasting, building and analysis of single-equation econometric models. Questions also cover elasticity, average and marginal characteristics, product and cost functions, measurement of monopoly power, supply, demand and equilibrium price, consumer and product surplus, etc. Several approaches are used to enable the required numerical computations in DisPeL - integration of third-party mathematical libraries, developing our own procedures from scratch, and wrapping our legacy math codes in order to modernize and reuse them.
With string model to time series forecasting
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.
Modelling the world oil market: Assessment of a quarterly econometric model
International Nuclear Information System (INIS)
Dees, Stephane; Karadeloglou, Pavlos; Kaufmann, Robert K.; Sanchez, Marcelo
2007-01-01
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
J Jeuck; F. Cubbage; R. Abt; R. Bardon; J. McCarter; J. Coulston; M. Renkow
2014-01-01
: We conducted a meta-analysis on 64 econometric models from 47 studies predicting forestland conversion to agriculture (F2A), forestland to development (F2D), forestland to non-forested (F2NF) and undeveloped (including forestland) to developed (U2D) land. Over 250 independent econometric variables were identified from 21 F2A models, 21 F2D models, 12 F2NF models, and...
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
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......, the proposed model is able to perform point forecasts as accurately as other methods based on machine learning techniques. Moreover, the recursive ARMA-GARCH model is easier to set-up and it gives additional information about the uncertainty of the forecasts. Even if some strong assumption has been made...
Wine market prices and investment under uncertainty: an econometric model for Bordeaux Crus Classes
Jones, Gregory V.; Storchmann, Karl-Heinz
2001-01-01
This paper describes an econometric assessment of wine market prices for 21 of the Crus Classes chateaux in the Bordeaux region of France. The model developed in the analysis attempts to define the relationship between factors that influence wine quality and those that influence wine prices. Characteristics of the models are: (1) climate influences on grape composition (acid and sugar levels), (2) grape composition influences on market prices, (3) subjective quality evaluations (Parker-points...
Rudzkis, R.; Uzdanaviciute, R.
2013-01-01
The paper is meant for econometric modeling and prediction of sector in- dice variation regularities of stock prices in the OMX exchange of the Baltic countries’ companies.To develop regression models, quarterly time series of 2000 - 2011 years are used.Regression equations, obtained in the work, allow us to name the basic macroeconomic indicators that significantly influence stock mar- ket fluctuations and to quantitatively estimate their various impact on stock in- dices c...
An economic framework for forecasting land-use and ecosystem change
International Nuclear Information System (INIS)
Lewis, David J.
2010-01-01
This paper develops a joint econometric-simulation framework to forecast detailed empirical distributions of the spatial pattern of land-use and ecosystem change. In-sample and out-of-sample forecasting tests are used to examine the performance of the parcel-scale econometric and simulation models, and the importance of multiple forecasting challenges is assessed. The econometric-simulation method is integrated with an ecological model to generate forecasts of the probability of localized extinctions of an amphibian species. The paper demonstrates the potential of integrating economic and ecological models to generate ecological forecasts in the presence of alternative market conditions and land-use policy constraints. (author)
Predicting future forestland area: a comparison of econometric approaches.
SoEun Ahn; Andrew J. Plantinga; Ralph J. Alig
2000-01-01
Predictions of future forestland area are an important component of forest policy analyses. In this article, we test the ability of econometric land use models to accurately forecast forest area. We construct a panel data set for Alabama consisting of county and time-series observation for the period 1964 to 1992. We estimate models using restricted data sets-namely,...
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.
Macroeconomic models, forecasting, and policymaking
Pescatori, Andrea; Zaman, Saeed
2011-01-01
Models of the macroeconomy have gotten quite sophisticated, thanks to decades of development and advances in computing power. Such models have also become indispensable tools for monetary policymakers, useful both for forecasting and comparing different policy options. Their failure to predict the recent financial crisis does not negate their use, it only points to some areas that can be improved.
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.
An assessment of econometric models applied to fossil fuel power generation
International Nuclear Information System (INIS)
Gracceva, F.; Quercioli, R.
2001-01-01
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 [it
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
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.
On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models
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...
Electricity generation modeling and photovoltaic forecasts in China
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.
Operational models for forecasting Dst
Watanabe, S.; Sagawa, E.; Ohtaka, K.; Shimazu, H.
We have constructed operational models for forecasting the geomagnetic storm index (Dst) two hours in advance from six parameters: the velocity and density of the solar wind, the magnitude of the interplanetary magnetic field (IMF), and the x, y, and z components of the IMF. Our models use an Elman-type neural network, and we forecast space weather by using real-time solar-wind data from the Advanced Composition Explorer spacecraft.The models have worked well since April of 1998 and the Dst values forecast using them have been made available to the public at http://www.crl.go.jp/uk/uk223/service/nnw/index.html. From February to October 1998 there were 11 storms with minimum Dst values below -80 nT, and for ten the difference between the forecast minimum Dst and the Dst calculated from data measured by ground stations was less than 23%.For the storm starting on 19 October, however, the difference was 40% because of the weak correlation between the ACE environment and the earth's environment during this event.The Dst depends on the orientation of the IMF relative to the solar magnetospheric x-y plane and seems to be relatively large when the y component of the IMF is positive and perhaps also when the x component is positive.
International Nuclear Information System (INIS)
Mankin, Shuichi; Yamazaki, Shigeki.
1985-11-01
A Long Term Macro Econometric Model (LTMEMO) has been developed for the purpose of generating economic scenarios for strategic analysis and for cost assessments of technologies in the field of nuclear research and development. The program system of the model is composed of such sub-programs as related social and economic statistic data base and its treatment program, identification and estimation programs of various econometric functions, simulation programs for future projections, and a reference econometric model program. The reference econometric model in the program system would be improved and modified easily by using data base and other sub-programs as the purpose of data retrieval, application of economic hypothesis, and scenario generation. The reference model belongs to a category of such standard types as macro-econometric, deterministic, and descriptive one, however, it was deviated based on the combination of Keynesian theories and Neo-classical theories and was modified by system engineering aspects. The model obtained good performances in such various econometric tests as statistical examinations in parameter estimation of each functions and so called partial tests, total tests, and final tests. Macro economic scenarios α and β, long term projections through 2030 of macro economy in our country were evaluated appropriately by this model. This report describes the process in the development of the model from needs of econometric model in nuclear fields to examples of economic scenarios generated by this model. Some consideration are taken into descriptions on the deviation of each functions and on the application of economic theories for practical use of this program system at the time of modification and improvements of the reference model. (author)
Econometric modelling of certain nuclear power systems based on thermal and fast breeder reactors
International Nuclear Information System (INIS)
Pavelescu, M.; Pioaru, C.; Ursu, I.
1988-01-01
Certain known economic analysis models for a LMFBR fast breeder and CANDU thermal solitary reactors are presented, based on the concepts of discounting and levelization. These models are subsequently utilized as a basis for establishing an original model for the econometric analysis of certain thermal reactor systems or/and fast breeder reactors. Case studies are subsequently conducted with the systems: 1-CANDU, 2-LMFBR, 3-CANDU + LMFBR which enables us to draw certain interesting conclusions for a long range nuclear power policy. (author)
A revised econometric model of the domestic pallet market
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...
Mesoscale model forecast verification during monsoon 2008
Indian Academy of Sciences (India)
The systematic error in the 850 hPa temperature indicates that largely the WRF model forecasts feature warm bias and the MM5 model forecasts feature cold bias. Features common to all the three models include warm bias over northwest India and cold bias over southeast peninsula. The 850 hPa specific humidity forecast ...
Characterizations of identified sets delivered by structural econometric models
Chesher, Andrew; Rosen, Adam M.
2016-01-01
This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous and/or discrete variables. Multiple values of unobserved variables can be associated with particular combinations of observed variables. This can arise when there are multiple sources of heterogeneity, censored or discrete endogenous variables, or inequality restrictions on functions of observed and unobserved variables. The models generalize t...
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 ...
An Econometric Model of Healthcare Demand With Nonlinear Pricing.
Kunz, Johannes S; Winkelmann, Rainer
2017-06-01
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. Copyright © 2016 John Wiley & Sons, Ltd.
Use of econometric models to estimate expenditure shares.
Trogdon, Justin G; Finkelstein, Eric A; Hoerger, Thomas J
2008-08-01
To investigate the use of regression models to calculate disease-specific shares of medical expenditures. Medical Expenditure Panel Survey (MEPS), 2000-2003. Theoretical investigation and secondary data analysis. Condition files used to define the presence of 10 medical conditions. Incremental effects of conditions on expenditures, expressed as a fraction of total expenditures, cannot generally be interpreted as shares. When the presence of one condition increases treatment costs for another condition, summing condition-specific shares leads to double-counting of expenditures. Condition-specific shares generated from multiplicative models should not be summed. We provide an algorithm that allows estimates based on these models to be interpreted as shares and summed across conditions.
An Econometric Model of the Low-Skill Labor Market
Crandall, Robert W.; And Others
1975-01-01
The model describes the demand and supply of low-skill labor (private household workers, other service workers, and nonfarm laborers) by State, based on the March 1970 Current Population Survey for 43 States and groups of States by a simultaneous-equations method. (Author/EA)
Modelling internal migration in Kenya: an econometric analysis with limited data.
Barber, G M; Milne, W J
1988-09-01
"In this paper the determinants of internal migration in Kenya are analyzed on the basis of a human capital model. Explanatory variables included in the specification are both economic (wage rates and employment rates) and noneconomic (for example, population density and educational attainment). Also incorporated are variables which reflect intervening opportunities.... The econometric results show that destination variables are important determinants of internal migration, as is distance between the districts. Further, the variables for the intervening opportunities add significantly to the explanatory power of the model." excerpt
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...
Interval Forecast for Smooth Transition Autoregressive Model ...
African Journals Online (AJOL)
In this paper, we propose a simple method for constructing interval forecast for smooth transition autoregressive (STAR) model. This interval forecast is based on bootstrapping the residual error of the estimated STAR model for each forecast horizon and computing various Akaike information criterion (AIC) function. This new ...
A dynamic econometric model of agricultural wage determination in Bangladesh.
Boyce, J K; Ravallion, M
1991-11-01
Economists applied data from 1949-1950 and 1980-1981 to a new dynamic model to examine the dynamics of determinants of agricultural wages in Bangladesh, particularly the effect of changes in relative prices of rice (the staple food) and productivity. Just a 20% rise in the price or rice was passed on in the agricultural wage rate within the current year. About 50% was passed on in the long run, however. Therefore an increase in the price of rice reduced the rice purchasing power of agricultural wages in the short and long term. In fact, the importance given to rice in the long run real wage rate was almost the same as the mean proportion of expenditure that an agricultural laborer in Bangladesh committed to rice and closely related food staples. Thus arise in the price of rice in comparison to other goods had limited effects on the long run real wage in terms of the bundle of goods typically consumed, but very adverse effects in the short run placing a high burden on the rural poor. On the other hand, the long run real wage rate fell considerably between the mid 1960s-early 1980s when overall agricultural productivity increased. The economists pointed out that this increased productivity may not have lowered long run real wage rates, but instead mitigating factors may have contributed to this fall. For example, population growth, rising landlessness, and insufficient economic growth in nonagricultural sectors resulted in a consistent growth in the labor supply. In conclusion, this new dynamic model showed that Bangladesh cannot depend only on agricultural growth to reduce the poverty of farmers.
ECONOMETRIC MODELING OF GDP BY EMPLOYMENT AND THE VALUE OF TANGIBLE FIXED ASSESTS
Directory of Open Access Journals (Sweden)
Cristina BURGHELEA
2015-04-01
Full Text Available The economic potential of a country is consistently a primary goal of existence and sustainable development, to ensure the livelihood of all residents, increase living standards. To achieve this major goal rigorous study must be complex to formulate a diagnosis and real economic status and rationale, on the basis of economic and legislative policy decisions, decisions addressing both immediate time horizons as well as longer periods of time. In this context, we analyzed dynamics of GDP according to the dynamics of employment and dynamics of tangible fixed assets of the economy by applying a rigorous econometric modeling methodology.
Much ado about two: reconsidering retransformation and the two-part model in health econometrics.
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.
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.
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
New interval forecast for stationary autoregressive models ...
African Journals Online (AJOL)
In this paper, we proposed a new forecasting interval for stationary Autoregressive, AR(p) models using the Akaike information criterion (AIC) function. Ordinarily, the AIC function is used to determine the order of an AR(p) process. In this study however, AIC forecast interval compared favorably with the theoretical forecast ...
Forecasting Market Shares from Models for Sales
D. Fok (Dennis); Ph.H.B.F. Franses (Philip Hans)
2000-01-01
textabstractDividing forecasts of brand sales by a forecast of category sales, when they are generated from brand specific sales-response models, renders biased forecasts of the brands' market shares. In this paper we therefore propose an easy-to-apply simulation-based method which results in
Electricity consumption forecasting in Italy using linear regression models
International Nuclear Information System (INIS)
Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio
2009-01-01
The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)
Modeling and Forecasting Persistent Financial Durations
Czech Academy of Sciences Publication Activity Database
Žikeš, F.; Baruník, Jozef; Shenai, N.
2017-01-01
Roč. 36, č. 10 (2017), s. 1081-1110 ISSN 0747-4938 R&D Projects: GA ČR GA13-32263S EU Projects: European Commission 612955 - FINMAP Institutional support: RVO:67985556 Keywords : price durations * long memory * multifractal models * realized volatility * Whittle estimation Subject RIV: AH - Economics OBOR OECD: Applied Economics, Econometrics Impact factor: 1.333, year: 2016 http://library.utia.cas.cz/separaty/2014/E/barunik-0434201.pdf
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.
Econometrics Models for Copper Recovery: A Case Study of North Waziristan-Copper Deposits
International Nuclear Information System (INIS)
Ali, S.; Khan, M.M.
2010-01-01
Fourteen econometrics models have been developed to evaluate the effects of various flotation process variables like, Propyl xanthate (X/sub 1/g/tonne), pH (X/sub 2/,) Sodium Cyanide (X/sub 3/ g/tonne), Sodium sulphide (X/sub 4/ g/tonne), Frother (X/sub 5/ g/tonne), Pulp density (X/sub 6/ w/vol), and Conditioning time (X/sub 7/ minute) on the copper recovery YR North Waziristan-NWFP Pakistan. Ordinary Least Square OLS method has been applied as an analytical technique for regression analysis. It has been concluded in this study that model given in equation 7 is best model among all. This equation shows that with the increase of one unit of X/sub 1/, Y/sub R/ will increase 0.05 units keeping all other variables constant. (author)
New Employment Forecasts. Hotel and Catering Industry 1988-1993.
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,…
Probabilistic Solar Forecasting Using Quantile Regression Models
Directory of Open Access Journals (Sweden)
Philippe Lauret
2017-10-01
Full Text Available In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS, a Numerical Weather Prediction (NWP model maintained by the European Center for Medium-Range Weather Forecast (ECMWF. Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions.
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.
Waste production and regional growth of marine activities an econometric model.
Bramati, Maria Caterina
2016-11-15
Coastal regions are characterized by intense human activity and climatic pressures, often intensified by competing interests in the use of marine waters. To assess the effect of public spending on the regional economy, an econometric model is here proposed. Not only are the regional investment and the climatic risks included in the model, but also variables related to the anthropogenic pressure, such as population, economic activities and waste production. Feedback effects of economic and demographic expansion on the pollution of coastal areas are also considered. It is found that dangerous waste increases with growing shipping and transportation activities and with growing population density in non-touristic coastal areas. On the other hand, the amount of non-dangerous wastes increases with marine mining, defense and offshore energy production activities. However, lower waste production occurs in areas where aquaculture and touristic industry are more exploited, and accompanied by increasing regional investment in waste disposal. Copyright © 2016 Elsevier Ltd. All rights reserved.
Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM
Directory of Open Access Journals (Sweden)
Nalan Baştürk
2016-03-01
Full Text Available This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of the target density is required. The approximation can be used as a candidate density in Importance Sampling or Metropolis Hastings methods for Bayesian inference on model parameters and probabilities. We present and discuss four canonical econometric models using a Graphics Processing Unit and a multi-core Central Processing Unit version of the MitISEM algorithm. The results show that the parallelization of the MitISEM algorithm on Graphics Processing Units and multi-core Central Processing Units is straightforward and fast to program using MATLAB. Moreover the speed performance of the Graphics Processing Unit version is much higher than the Central Processing Unit one.
Mullahy, John
2009-07-01
Econometric modeling of healthcare costs and expenditures has become an important component of decision-making across a wide array of real-world settings. The objective of this article is to provide a brief summary of important conceptual and analytical issues involved in econometric healthcare cost modeling. To this end, the article explores: outcome measures typically analyzed in such work; the decision maker's perspective in econometric cost modeling exercises; specific analytical issues in econometric model specification; statistical goodness-of-fit testing; empirical implications of "upper tail" (or "high cost") phenomena; and issues relating to the reporting of findings. Some of the concepts explored here are illustrated in light of samples drawn from the 2005 Medical Expenditure Panel Survey and the 2005 Nationwide Inpatient Sample. Analysts of healthcare cost data have at their disposal an increasingly sophisticated tool kit for analyzing such data that can in principle and in fact yield increasingly interesting insights into data structures. Yet for such analyses to usefully inform policy decisions, the manner in which such studies are designed, undertaken, and reported must accommodate considerations relevant to the decision-making community. The article concludes with some preliminary thoughts on how such bridges might be constructed.
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.
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
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 ...... for educational groups, where the initial forecast year is a change point for unemployment....
Midway Tsunami Forecast Grids for MOST Model
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...
On Long Memory Origins and Forecast Horizons
DEFF Research Database (Denmark)
Vera-Valdés, J. Eduardo
Most long memory forecasting studies assume that the memory is generated by the fractional difference operator. We argue that the most cited theoretical arguments for the presence of long memory do not imply the fractional difference operator, and assess the performance of the autoregressive...... for, among others, Climate Econometrics and Financial Econometrics models dealing with long memory series at different forecast horizons. We show in an example that while a short memory autoregressive moving average (ARMA) model gives the best performance when forecasting the Realized Variance...... fractionally integrated moving average (ARFIMA) model when forecasting series with long memory generated by nonfractional processes. We find that high-order autoregressive (AR) models produce similar or superior forecast performance than ARFIMA models at short horizons. Nonetheless, as the forecast horizon...
Demand forecast model based on CRM
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.
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.
Forecasting models for national economic planning
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...
NAVO NCOM Relocatable Model: Fukushima Regional Forecast
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...
The Standard Model in the history of the Natural Sciences, Econometrics, and the social sciences
International Nuclear Information System (INIS)
Fisher, W P Jr
2010-01-01
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.
The Standard Model in the history of the Natural Sciences, Econometrics, and the social sciences
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.
Combining SKU-level sales forecasts from models and experts
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
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.
An Assessment of Japanese Carbon Tax Reform Using the E3MG Econometric Model
Lee, Soocheol; Pollitt, Hector; Ueta, Kazuhiro
2012-01-01
This paper analyses the potential economic and environmental effects of carbon taxation in Japan using the E3MG model, a global macroeconometric model constructed by the University of Cambridge and Cambridge Econometrics. The paper approaches the issues by considering first the impacts of the carbon tax in Japan introduced in 2012 and then the measures necessary to reduce Japan's emissions in line with its Copenhagen pledge of −25% compared to 1990 levels. The results from the model suggest that FY2012 Tax Reform has only a small impact on emission levels and no significant impact on GDP and employment. The potential costs of reducing emissions to meet the 25% reduction target for 2020 are quite modest, but noticeable. GDP falls by around 1.2% compared to the baseline and employment by 0.4% compared to the baseline. But this could be offset, with some potential economic benefits, if revenues are recycled efficiently. This paper considers two revenue recycling scenarios. The most positive outcome is if revenues are used both to reduce income tax rates and to increase investment in energy efficiency. This paper shows there could be double dividend effects, if Carbon Tax Reform is properly designed. PMID:23365531
An assessment of Japanese carbon tax reform using the E3MG econometric model.
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.
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.
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.
Description of the Battlescale Forecast Model
National Research Council Canada - National Science Library
Henmi, Teizi
1998-01-01
.... Army Integrated Meteorological System Block II software. The Battlescale Forecast Model can be used operationally over any part of the world by using meteorological data obtained through the Automated Weather Distribution System...
Pollen Forecast and Dispersion Modelling
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
Weather forecasting based on hybrid neural model
Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.
2017-11-01
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.
2011-09-21
Title: Transportation and Socioeconomic Impacts of Bypasses on Communities: An Integrated Synthesis of Panel Data, Multilevel, and Spatial Econometric Models with Case Studies. The title used at the start of this project was Transportation and Soc...
Multicomponent ensemble models to forecast induced seismicity
Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.
2018-01-01
In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels
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.
Econometric models for distinguishing between market-driven and publicly-funded energy efficiency
International Nuclear Information System (INIS)
Horowitz, Marvin J.
2005-01-01
Central to the problem of estimating energy program benefits is the necessity to differentiate between changes in energy use that would have occurred in the absence of public programs versus declines in energy use that would not have occurred but for public programs. The former changes are often referred to as naturally-occurring or market-driven effects. They occur due to a combination of one or more independent variables, such as changes in prices, incomes, weather, and technology. For a rigorous, scientifically-valid program evaluation, it is essential to first control for these variables before making statistical inferences related to public program effects. This paper describes the economic and statistical issues surrounding quantitative studies of energy use, energy efficiency, and public programs. To illustrate the strengths and weaknesses of different impact evaluation approaches, this paper describes three new studies related to electricity use in the U. S. commercial buildings sector. Specification and estimation of time series and cross section econometric models are discussed, as are their capabilities for obtaining long-run estimates of the net impacts of energy efficiency programs
Camenzind, Paul A
2012-03-13
In spite of a detailed and nation-wide legislation frame, there exist large cantonal disparities in consumed quantities of health care services in Switzerland. In this study, the most important factors of influence causing these regional disparities are determined. The findings can also be productive for discussing the containment of health care consumption in other countries. Based on the literature, relevant factors that cause geographic disparities of quantities and costs in western health care systems are identified. Using a selected set of these factors, individual panel econometric models are calculated to explain the variation of the utilization in each of the six largest health care service groups (general practitioners, specialist doctors, hospital inpatient, hospital outpatient, medication, and nursing homes) in Swiss mandatory health insurance (MHI). The main data source is 'Datenpool santésuisse', a database of Swiss health insurers. For all six health care service groups, significant factors influencing the utilization frequency over time and across cantons are found. A greater supply of service providers tends to have strong interrelations with per capita consumption of MHI services. On the demand side, older populations and higher population densities represent the clearest driving factors. Strategies to contain consumption and costs in health care should include several elements. In the federalist Swiss system, the structure of regional health care supply seems to generate significant effects. However, the extent of driving factors on the demand side (e.g., social deprivation) or financing instruments (e.g., high deductibles) should also be considered.
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...
Modeling and forecasting petroleum futures volatility
International Nuclear Information System (INIS)
Sadorsky, Perry
2006-01-01
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)
Forecasting with nonlinear time series models
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Teräsvirta, Timo
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......In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...
NEW CAR DEMAND MODELING AND FORECASTING USING BASS DIFFUSION MODEL
Zuhaimy Ismail; Noratikah Abu
2013-01-01
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...
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).
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...
Experts' adjustment to model-based forecasts: Does the forecast horizon matter?
Ph.H.B.F. Franses (Philip Hans); R. Legerstee (Rianne)
2007-01-01
textabstractExperts may have domain-specific knowledge that is not included in a statistical model and that can improve forecasts. While one-step-ahead forecasts address the conditional mean of the variable, model-based forecasts for longer horizons have a tendency to convert to the unconditional
Convolution copula econometrics
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.
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.
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
Municipal water consumption forecast accuracy
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.
Uranium price forecasting methods
International Nuclear Information System (INIS)
Fuller, D.M.
1994-01-01
This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again
Modelling and Forecasting the Capsized Market Spot Freight Rate ...
African Journals Online (AJOL)
An investor in this volatile market will find it very difficult for him to succeed by making a good decision. Most of the companies are faced with high risk of collapse if the managers are uncertain about the future. The study employed two econometrics models; Error Correction (EC) model and ARMA (Auto Regressive Moving ...
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...
Mesoscale model forecast verification during monsoon 2008
Indian Academy of Sciences (India)
There have been very few mesoscale modelling studies of the Indian monsoon, with focus on the verification and intercomparison of the operational real time forecasts. With the exception of Das et al (2008), most of the studies in the literature are either the case studies of tropical cyclones and thunderstorms or the sensitivity ...
Two quantitative forecasting methods for macroeconomic indicators in Czech Republic
Directory of Open Access Journals (Sweden)
Mihaela BRATU (SIMIONESCU
2012-03-01
Full Text Available Econometric modelling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in Czech Republic some accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2 models, ARMA procedure and models with lagged variables. One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions more influenced by the recent evolution of the indicators.
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.
McNown, Robert F.; Hunt, Gary L.
1984-01-01
Describes nine experiments designed to illustrate different applied statistical problems or econometric techniques suitable for the first course in econometrics. Additional information and computer programs are available from the authors. (Author/RM)
Evaluation of the Mountain Wave Forecast Model's Stratospheric Turbulence Simulations
National Research Council Canada - National Science Library
Allen, Mark
2003-01-01
.... The Air Force Weather Agency (AFWA) requested a product with the capability of forecasting Stratoturb at 30, 50, and 70 mb using model data currently available, To facilitate their request, the Mountain Wave Forecast Model (MWFM...
ECONOMETRIC TOOLS OF CONTROLLING
Orlov A. I.
2015-01-01
Econometrics is one of the most effective mathematical tools of controlling. The article deals with general problems of application of econometric methods in solving problems of controlling. Econometric methods - is primarily a statistical analysis of concrete economic data, of course, with the help of computers. In our country, they are still relatively little known, even though we have the most powerful scientific school in the foundations of econometrics - the probability theory. The artic...
Generalized martingale model of the uncertainty evolution of streamflow forecasts
Zhao, Tongtiegang; Zhao, Jianshi; Yang, Dawen; Wang, Hao
2013-07-01
Streamflow forecasts are dynamically updated in real-time, thus facilitating a process of forecast uncertainty evolution. Forecast uncertainty generally decreases over time and as more hydrologic information becomes available. The process of forecasting and uncertainty updating can be described by the martingale model of forecast evolution (MMFE), which formulates the total forecast uncertainty of a streamflow in one future period as the sum of forecast improvements in the intermediate periods. This study tests the assumptions, i.e., unbiasedness, Gaussianity, temporal independence, and stationarity, of MMFE using real-world streamflow forecast data. The results show that (1) real-world forecasts can be biased and tend to underestimate the actual streamflow, and (2) real-world forecast uncertainty is non-Gaussian and heavy-tailed. Based on these statistical tests, this study proposes a generalized martingale model GMMFE for the simulation of biased and non-Gaussian forecast uncertainties. The new model combines the normal quantile transform (NQT) with MMFE to formulate the uncertainty evolution of real-world streamflow forecasts. Reservoir operations based on a synthetic forecast by GMMFE illustrates that applications of streamflow forecasting facilitate utility improvements and that special attention should be focused on the statistical distribution of forecast uncertainty.
Forecasting Canadian nuclear power station construction costs
International Nuclear Information System (INIS)
Keng, C.W.K.
1985-01-01
Because of the huge volume of capital required to construct a modern electric power generating station, investment decisions have to be made with as complete an understanding of the consequences of the decision as possible. This understanding must be provided by the evaluation of future situations. A key consideration in an evaluation is the financial component. This paper attempts to use an econometric method to forecast the construction costs escalation of a standard Canadian nuclear generating station (NGS). A brief review of the history of Canadian nuclear electric power is provided. The major components of the construction costs of a Canadian NGS are studied and summarized. A database is built and indexes are prepared. Based on these indexes, an econometric forecasting model is constructed using an apparently new econometric methodology of forecasting modelling. Forecasts for a period of 40 years are generated and applications (such as alternative scenario forecasts and range forecasts) to uncertainty assessment and/or decision-making are demonstrated. The indexes, the model, and the forecasts and their applications, to the best of the author's knowledge, are the first for Canadian NGS constructions. (author)
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.
Szolgayova, Elena
2010-05-01
hydrological models. However, the GARCH family of models proved to be suited in removing it only in daily time step. The basic GARCH model was not applicable on any of the time series. In all other investigated cases, the EGARCH(1,1) model had to be used. Unlike in econometric time series, where the so called leverage effect (i.e. the series reacts more strongly to negative changes) is present and pointed out by this model, here the data tends to react more strongly on positive changes. In this particular case it was found, that the general property of hydrological processes, that the rise of discharge is rainfall driven (a highly nonlinear chaotic intermittent process) and the decrease of discharge is ruled by the damping effects of the water storage in the driven system (catchment or river reach), is present also in the hydrological model error series. This shows, that the modelling and forecasting of floods (pulse like rising discharge) is a more demanding task than that of droughts (slowly decreasing flows). Even though the GARCH models did show partial improvements in the modelling and forecasting of flows, they still have several serious disadvantages (such as high sensitivity to the chosen fitting period) and possible further use should be further investigated. These results are of importance with respect to future attempts of modelling of error time series of hydrological models in such hybrid frameworks. They underpin the need of a non-mechanistic approach in the case based analysis of such data and the physical interpretation of statistical modelling results.
Bond, Lyndal; Hilton, Shona
2014-01-01
Background: Novel policy interventions may lack evaluation-based evidence. Considerations to introduce minimum unit pricing (MUP) of alcohol in the UK were informed by econometric modelling (the ‘Sheffield model’). We aim to investigate policy stakeholders’ views of the utility of modelling studies for public health policy. Methods: In-depth qualitative interviews with 36 individuals involved in MUP policy debates (purposively sampled to include civil servants, politicians, academics, advocates and industry-related actors) were conducted and thematically analysed. Results: Interviewees felt familiar with modelling studies and often displayed detailed understandings of the Sheffield model. Despite this, many were uneasy about the extent to which the Sheffield model could be relied on for informing policymaking and preferred traditional evaluations. A tension was identified between this preference for post hoc evaluations and a desire for evidence derived from local data, with modelling seen to offer high external validity. MUP critics expressed concern that the Sheffield model did not adequately capture the ‘real life’ world of the alcohol market, which was conceptualized as a complex and, to some extent, inherently unpredictable system. Communication of modelling results was considered intrinsically difficult but presenting an appropriate picture of the uncertainties inherent in modelling was viewed as desirable. There was general enthusiasm for increased use of econometric modelling to inform future policymaking but an appreciation that such evidence should only form one input into the process. Conclusion: Modelling studies are valued by policymakers as they provide contextually relevant evidence for novel policies, but tensions exist with views of traditional evaluation-based evidence. PMID:24367068
International Nuclear Information System (INIS)
Adams, F. Gerard; Shachmurove, Yochanan
2008-01-01
The Chinese economy is in a stage of energy transition: from low efficiency solid fuels to oil, gas, and electric power, from agriculture to urbanization and industrialization, from heavy industry to lighter and high tech industry, from low motorization to rapid growth of the motor vehicle population. Experts fear that continued rapid economic growth in China will translate into a massive need to expand imports of oil, coal, and gas. We build an econometric model of the Chinese energy economy based on the energy balance. We use that model to forecast Chinese energy consumption and imports to 2020. The study suggests that China will, indeed, require rapidly growing imports of oil, coal, and gas. This growth is not so sensitive to the rate of economic growth as to increases in motorization. It can be offset, but probably only in small part, by increasing domestic energy production or by improvements in the efficiency of use, particularly in the production of electric power. (author)
AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS
Directory of Open Access Journals (Sweden)
Н. Білак
2012-04-01
Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.
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
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Oahu
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...
Weather Research and Forecasting (WRF) Regional Atmospheric Model: CNMI
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...
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Guam
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...
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Samoa
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...
forecasting with nonlinear time series model: a monte-carlo
African Journals Online (AJOL)
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erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.
Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice
Callot, Laurent A.F.; Kock, Anders B.; Medeiros, Marcelo C.
2017-01-01
We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso-type estimators to reduce the dimensionality and provide strong theoretical guarantees on the forecast capability of our procedure. We show that we can forecast
Improved Forecasting Methods for Naval Manpower Studies
2015-03-25
Structural Change Models, Journal of Applied Econometrics 18: 1-22. [10] Caporale T., Grier K. 2005, How Smart is my Dummy? Time Series Test for the...Tennessee 38055-1000 www.nprst.navy.mil NPRST-TR-15-3 March 2015 Improved Forecasting Methods for Naval Manpower Studies Ping Ying Bellamy...Ph.D. Tanja F. Blackstone, Ph.D. Navy Personnel Research, Studies, and Technology NPRST-TR-15-3 March 2015 Improved Forecasting Methods
The Red Sea Modeling and Forecasting System
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
Development of a sales forecasting model for canopy windows
2014-01-01
M.Com. (Business Management) Forecasting is an important function used in a wide range of business planning or decision-making situations. The purpose ofthis study was to build a sales forecasting model that would be practical and cost effective, from the various forecasting methods and techniques available. Various forecast models, methods and techniques are outlined in the initial part of this study by the author. The author has outlined some of the fundamentals and limitations that unde...
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.
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
Castro Heredia, L. M.; Suarez, F. I.; Fernandez, B.; Maass, T.
2016-12-01
For forecasting of water resources, weather outputs provide a valuable source of information which is available online. Compared to traditional ground-based meteorological gauges, weather forecasts data offer spatially and temporally continuous data not yet evaluated and used in the forecasting of water resources in mountainous regions in Chile. Nevertheless, the use of this non-conventional data has been limited or null in developing countries, basically because of the spatial resolution, despite the high potential in the management of water resources. The adequate incorporation of these data in hydrological models requires its evaluation while taking into account the features of river basins in mountainous regions. This work presents an integrated forecasting system which represents a radical change in the way of making the streamflow forecasts in Chile, where the snowmelt forecast is the principal component of water resources management. The integrated system is composed of a physically based hydrological model, which is the prediction tool itself, together with a methodology for remote sensing data gathering that allows feed the hydrological model in real time, and meteorological forecasts from NCEP-CFSv2. Previous to incorporation of meteorological forecasts into the hydrological model, the weather outputs were evaluated and downscaling using statistical downscaling methods. The hydrological forecasts were evaluated in two mountain basins in Chile for a term of six months for the snowmelt period. In every month an assimilation process was performed, and the hydrological forecast was improved. Each month, the snow cover area (from remote sensing) and the streamflow observed were used to assimilate the model parameters in order to improve the next hydrological forecast using meteorological forecasts. The operation of the system in real time shows a good agreement between the streamflow and the snow cover area observed. The hydrological model and the weather
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...
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.
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.
Forecasting with nonlinear time series model: A Monte-Carlo ...
African Journals Online (AJOL)
In this paper, we propose a new method of forecasting with nonlinear time series model using Monte-Carlo Bootstrap method. This new method gives better result in terms of forecast root mean squared error (RMSE) when compared with the traditional Bootstrap method and Monte-Carlo method of forecasting using a ...
Causal inference in econometrics
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.
Three essays in econometric theory
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
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.
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.
Econometric modelling of Serbian current account determinants: Jackknife Model Averaging approach
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Petrović Predrag
2014-01-01
Full Text Available This research aims to model Serbian current account determinants for the period Q1 2002 - Q4 2012. Taking into account the majority of relevant determinants, using the Jackknife Model Averaging approach, 48 different models have been estimated, where 1254 equations needed to be estimated and averaged for each of the models. The results of selected representative models indicate moderate persistence of the CA and positive influence of: fiscal balance, oil trade balance, terms of trade, relative income and real effective exchange rates, where we should emphasise: (i a rather strong influence of relative income, (ii the fact that the worsening of oil trade balance results in worsening of other components (probably non-oil trade balance of CA and (iii that the positive influence of terms of trade reveals functionality of the Harberger-Laursen-Metzler effect in Serbia. On the other hand, negative influence is evident in case of: relative economic growth, gross fixed capital formation, net foreign assets and trade openness. What particularly stands out is the strong effect of relative economic growth that, most likely, reveals high citizens' future income growth expectations, which has negative impact on the CA.
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
Solid waste forecasting using modified ANFIS modeling.
Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; K N A, Maulud
2015-10-01
Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98. To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.
Modeling olive-crop forecasting in Tunisia
Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji
2017-05-01
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.
Space weather: Modeling and forecasting ionospheric
International Nuclear Information System (INIS)
Calzadilla Mendez, A.
2008-01-01
Full text: Space weather is the set of phenomena and interactions that take place in the interplanetary medium. It is regulated primarily by the activity originating in the Sun and affects both the artificial satellites that are outside of the protective cover of the Earth's atmosphere as the rest of the planets in the solar system. Among the phenomena that are of great relevance and impact on Earth are the auroras and geomagnetic storms , these are a direct result of irregularities in the flow of the solar wind and the interplanetary magnetic field . Given the high complexity of the physical phenomena involved (magnetic reconnection , particle inlet and ionizing radiation to the atmosphere) one of the great scientific challenges today is to forecast the state of plasmatic means either the interplanetary medium , the magnetosphere and ionosphere , for their importance to the development of various human activities such as radio , global positioning , navigation, etc. . It briefly address some of the international ionospheric modeling methods and contributions and participation that currently has the space group of the Institute of Geophysics Geophysics and Astronomy (IGA) in these activities of modeling and forecasting ionospheric. (author)
INFERENCE AND SENSITIVITY IN STOCHASTIC WIND POWER FORECAST MODELS.
Elkantassi, Soumaya
2017-10-03
Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.
Pathways to a low-carbon economy for the UK with the macro-econometric E3MG model
International Nuclear Information System (INIS)
Dagoumas, A.S.; Barker, T.S.
2010-01-01
This paper examines different carbon pathways for achieving deep CO 2 reduction targets for the UK using a macro-econometric hybrid model E3MG, which stands for Energy-Economy-Environment Model at the Global level. The E3MG, with the UK as one of its regions, combines a top-down approach for modeling the global economy and for estimating the aggregate and disaggregate energy demand and a bottom-up approach (Energy Technology subModel, ETM) for simulating the power sector, which then provides feedback to the energy demand equations and the whole economy. The ETM submodel uses a probabilistic approach and historical data for estimating the penetration levels of the different technologies, considering their economic, technical and environmental characteristics. Three pathway scenarios (CFH, CLC and CAM) simulate the CO 2 reduction by 40%, 60% and 80% by 2050 compared to 1990 levels respectively and are compared with a reference scenario (REF), with no reduction target. The targets are modeled as the UK contribution to an international mitigation effort, such as achieving the G8 reduction targets, which is a more realistic political framework for the UK to move towards deep reductions rather than moving alone. This paper aims to provide modeling evidence that deep reduction targets can be met through different carbon pathways while also assessing the macroeconomic effects of the pathways on GDP and investment.
DEFF Research Database (Denmark)
Lyk-Jensen, Stéphanie
2011-01-01
model. The analysis uses a structural relationship to explain the structure of the exchange of the goods—a relationship that can be used in the year of forecast. This article also provides a new methodology for converting monetary aggregates into quantity aggregates. The resulting commodity growth rates....... This article models long-term dynamic physical trade flows and estimates a dynamic panel data model for foreign trade for the EU15 and two countries from the EFTA (European Free Trade Association) 1967–2002. The analysis suggests that a dynamic three-way-effects gravity equation is the best-fitted econometric...
Development of Ensemble Model Based Water Demand Forecasting Model
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)
Electricity price forecasting through transfer function models
International Nuclear Information System (INIS)
Nogales, F.J.; Conejo, A.J.
2006-01-01
Forecasting electricity prices in present day competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes a transfer function model to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build it. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The proposed model is compared with naive and other techniques. Journal of the Operational Research Society (2006) 57, 350-356.doi:10.1057/palgrave.jors.2601995; published online 18 May 2005. (author)
Statistical and RBF NN models : providing forecasts and risk assessment
Marček, Milan
2009-01-01
Forecast accuracy of economic and financial processes is a popular measure for quantifying the risk in decision making. In this paper, we develop forecasting models based on statistical (stochastic) methods, sometimes called hard computing, and on a soft method using granular computing. We consider the accuracy of forecasting models as a measure for risk evaluation. It is found that the risk estimation process based on soft methods is simplified and less critical to the question w...
Models of Investor Forecasting Behavior — Experimental Evidence
Directory of Open Access Journals (Sweden)
Federico Bonetto
2017-12-01
Full Text Available Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price bubbles, price crashes, and unpredictable price cycles occur. In this paper, we report the results of behavioral experiments involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast formation. We conducted three experimental sessions with different participants in each session. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past price changes, even when they know the fundamental value of the asset exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. The forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and cycles in dynamical systems models, and price bubbles and cycles ended up occurring in all three sessions.
A Hybrid Model for Forecasting Sales in Turkish Paint Industry
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Alp Ustundag
2009-12-01
Full Text Available Sales forecasting is important for facilitating effective and efficient allocation of scarce resources. However, how to best model and forecast sales has been a long-standing issue. There is no best forecasting method that is applicable in all circumstances. Therefore, confidence in the accuracy of sales forecasts is achieved by corroborating the results using two or more methods. This paper proposes a hybrid forecasting model that uses an artificial intelligence method (AI with multiple linear regression (MLR to predict product sales for the largest Turkish paint producer. In the hybrid model, three different AI methods, fuzzy rule-based system (FRBS, artificial neural network (ANN and adaptive neuro fuzzy network (ANFIS, are used and compared to each other. The results indicate that FRBS yields better forecasting accuracy in terms of root mean squared error (RMSE and mean absolute percentage error (MAPE.
Pretis, F.; 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 ...
Canadian nuclear power plant construction cost forecast and analysis
International Nuclear Information System (INIS)
Keng, C.W.K.
1985-01-01
Because of the huge volume of capital required to construct a modern electric power generating station, investment decisions have to be made with as complete an understanding of the consequence of the decision as possible. This understanding must be provided by the evaluation of the situation to take place in the future. This paper attempts to use an econometric method to forecast the construction costs escalation of a standard Canadian nuclear generating station (NGS). A review of the history of Canadian nuclear electric power is provided. The major components of the construction costs of a Canadian NGS are studied and summarized. A data base is built and indexes are prepared. Based on these indexes an econometric forecasting model is constructed using an apparently new econometric methodology of forecasting modelling. Forecasts for a period of forty years are generated and applications of alternative scenario forecasts and range forecasts to uncertainty assessment are demonstrated. The indexes, the model, and the forecasts and their applications, to the best of the author's knowledge, are the very first ever done for Canadian NGS constructions
Forecasting natural gas consumption in China by Bayesian Model Averaging
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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.
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.
Empirical evaluation of a forecasting model for successful facilitation ...
African Journals Online (AJOL)
The forecasting model identified 8 key attributes for facilitation success based on performance measures from the 1999 Facilitator Customer Service Survey. During 2000 the annual Facilitator Customer Satisfaction Survey was employed to validate the findings of the forecasting model. A total of 1910 questionnaires were ...
Development of rainfall-runoff forecast model | Oyebode | Journal of ...
African Journals Online (AJOL)
This study developed a neurofuzzy-based rainfall-runoff forecast model for river basin and evaluated the performance of the model. This was with a view to capturing the behaviour of hydrological and meterological variables involved in rainfall-runoff process to improve forecast accuracy of rainfallrunoff. Three hydrological ...
Honolulu, Hawaii Tsunami Forecast Grids for MOST Model
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...
King Cove, Alaska Tsunami Forecast Grids for MOST Model
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...
Hilo, Hawaii Tsunami Forecast Grids for MOST Model
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...
Haleiwa, Hawaii Tsunami Forecast Grids for MOST Model
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...
Kailua-Kona, Hawaii Tsunami Forecast Grids for MOST Model
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....
Keauhou, Hawaii Tsunami Forecast Grids for MOST Model
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...
Seward, Alaska Tsunami Forecast Grids for MOST Model
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...
Savannah, Georgia Tsunami Forecast Grids for MOST Model
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...
Montauk, New York Tsunami Forecast Grids for MOST Model
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...
Kodiak, Alaska Tsunami Forecast Grids for MOST Model
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...
Palm Beach, Florida Tsunami Forecast Grids for MOST Model
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....
Kihei, Hawaii Tsunami Forecast Grids for MOST Model
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...
Port Alexander, Alaska Tsunami Forecast Grids for MOST Model
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....
Shemya, Alaska Tsunami Forecast Grids for MOST Model
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...
Key West, Florida Tsunami Forecast Grids for MOST Model
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...
Mayaguez, Puerto Rico Tsunami Forecast Grids for MOST Model
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....
Apra Harbor, Guam Tsunami Forecast Grids for MOST Model
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...
Ponce, Puerto Rico Tsunami Forecast Grids for MOST Model
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....
Wake Island Tsunami Forecast Grids for MOST Model
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...
Florence, Oregon Tsunami Forecast Grids for MOST Model
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...
Sitka, Alaska Tsunami Forecast Grids for MOST Model
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...
San Juan, Puerto Rico Tsunami Forecast Grids for MOST Model
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....
Neah Bay, Washington Tsunami Forecast Grids for MOST Model
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....
Lahaina, Hawaii Tsunami Forecast Grids for MOST Model
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...
Seaside, Oregon Tsunami Forecast Grids for MOST Model
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...
Ocean City, Maryland Tsunami Forecast Grids for MOST Model
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....
San Diego, California Tsunami Forecast Grids for MOST Model
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....
Nikolski, Alaska Tsunami Forecast Grids for MOST Model
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...
Portland, Maine Tsunami Forecast Grids for MOST Model
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...
Kahului, Hawaii Tsunami Forecast Grids for MOST Model
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...
Adak, Alaska Tsunami Forecast Grids for MOST Model
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...
Pearl Harbor, Hawaii Tsunami Forecast Grids for MOST Model
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....
Port Orford, Oregon Tsunami Forecast Grids for MOST Model
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....
La Push, Washington Tsunami Forecast Grids for MOST Model
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....
Westport, Washington Tsunami Forecast Grids for MOST Model
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....
Arecibo, Puerto Rico Tsunami Forecast Grids for MOST Model
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....
Daytona Beach, Florida Tsunami Forecast Grids for MOST Model
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....
Hanalei, Hawaii Tsunami Forecast Grids for MOST Model
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...
Nawiliwili, Hawaii Tsunami Forecast Grids for MOST Model
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....
Newport, Oregon Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Newport, Oregon Forecast Model Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Application of grafted polynomial function in forecasting cotton ...
African Journals Online (AJOL)
A study was conducted to forecast cotton production trend with the application of a grafted polynomial function in Nigeria from 1985 through 2013. Grafted models are used in econometrics to embark on economic analysis involving time series. In economic time series, the paucity of data and their availability has always ...
Statistical parameters as a means to a priori assess the accuracy of solar forecasting models
International Nuclear Information System (INIS)
Voyant, Cyril; Soubdhan, Ted; Lauret, Philippe; David, Mathieu; Muselli, Marc
2015-01-01
In this paper we propose to determinate and to test a set of 20 statistical parameters in order to estimate the short term predictability of the global horizontal irradiation time series and thereby to propose a new prospective tool indicating the expected error regardless the forecasting methods used. The mean absolute log return, which is a tool usually used in econometrics but never in global radiation prediction, proves to be a very good estimator. Some examples of the use of this tool are exposed, showing the interest of this statistical parameter in concrete cases of predictions or optimizations. This study gives a judgment for engineers and researchers on the installation or management of solar plants and could help in minimizing the energy crisis allowing to improve the renewable energy part of the energy mix. - Highlights: • Use of statistical parameter never used for the global radiation forecasting. • A priori index allowing to optimize easily and quickly a clear sky model. • New methodology allowing to quantify the prediction error regardless the predictor used. • The prediction error depends more on the location and the time series type than the machine Learning method used.
On the clustering of climate models in ensemble seasonal forecasting
Yuan, Xing; Wood, Eric F.
2012-09-01
Multi-model ensemble seasonal forecasting system has expanded in recent years, with a dozen coupled climate models around the world being used to produce hindcasts or real-time forecasts. However, many models are sharing similar atmospheric or oceanic components which may result in similar forecasts. This raises questions of whether the ensemble is over-confident if we treat each model equally, or whether we can obtain an effective subset of models that can retain predictability and skill as well. In this study, we use a hierarchical clustering method based on inverse trigonometric cosine function of the anomaly correlation of pairwise model hindcasts to measure the similarities among twelve American and European seasonal forecast models. Though similarities are found between models sharing the same atmospheric component, different versions of models from the same center sometimes produce quite different temperature forecasts, which indicate that detailed physics packages such as radiation and land surface schemes need to be analyzed in interpreting the clustering result. Uncertainties in clustering for different forecast lead times also make reducing redundant models more complicated. Predictability analysis shows that multi-model ensemble is not necessarily better than a single model, while the cluster ensemble shows consistent improvement against individual models. The eight model-based cluster ensemble forecast shows comparable performance to the total twelve model ensemble in terms of probabilistic forecast skill for accuracy and discrimination. This study also manifests that models developed in U.S. and Europe are more independent from each other, suggesting the necessity of international collaboration in enhancing multi-model ensemble seasonal forecasting.
Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method
International Nuclear Information System (INIS)
Amjady, Nima; Keynia, Farshid
2008-01-01
In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of wavelet transform (WT) and a hybrid forecast method is proposed for this purpose. The hybrid method is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithms (EA). Both time domain and wavelet domain features are considered in a mixed data model for price forecast, in which the candidate input variables are refined by a feature selection technique. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. (author)
An Applied Physicist Does Econometrics
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? )
Regional Model Nesting Within GFS Daily Forecasts Over West Africa
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
Precipitation forecasts and their uncertainty as input into hydrological models
Directory of Open Access Journals (Sweden)
M. Kobold
2005-01-01
Full Text Available Torrential streams and fast runoff are characteristic of most Slovenian rivers and extensive damage is caused almost every year by rainstorms affecting different regions of Slovenia. Rainfall-runoff models which are tools for runoff calculation can be used for flood forecasting. In Slovenia, the lag time between rainfall and runoff is only a few hours and on-line data are used only for now-casting. Predicted precipitation is necessary in flood forecasting some days ahead. The ECMWF (European Centre for Medium-Range Weather Forecasts model gives general forecasts several days ahead while more detailed precipitation data with the ALADIN/SI model are available for two days ahead. Combining the weather forecasts with the information on catchment conditions and a hydrological forecasting model can give advance warning of potential flooding notwithstanding a certain degree of uncertainty in using precipitation forecasts based on meteorological models. Analysis of the sensitivity of the hydrological model to the rainfall error has shown that the deviation in runoff is much larger than the rainfall deviation. Therefore, verification of predicted precipitation for large precipitation events was performed with the ECMWF model. Measured precipitation data were interpolated on a regular grid and compared with the results from the ECMWF model. The deviation in predicted precipitation from interpolated measurements is shown with the model bias resulting from the inability of the model to predict the precipitation correctly and a bias for horizontal resolution of the model and natural variability of precipitation.
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.
Directory of Open Access Journals (Sweden)
Changqing Luo
2017-11-01
Full Text Available To understand the role of green credit in maintaining economic sustainability, we develop theoretical hypotheses including expectation, supervision and capital allocation channels to explain the impacts of green credit. Then, we use hybrid econometric models by using Chinese-listed enterprises in the energy-saving and environmental sectors from 2007 to 2015 as the research sample to verify the above hypotheses. The empirical results show that: (1 the average value of financial performance and operational efficiency is relatively low, and the endogenous abilities of those enterprises have not yet been established; (2 the issuance of green loans does not improve public expectations of enterprises in the green industry, thus the expectation channel is not supported; (3 the issuance of green loans does not necessarily improve the enterprise’s operational efficiency and financial performance, thus the supervision channel hypotheses are not supported; and (4 green loans lead to an increase in financing costs, management costs, operation costs, and expenditure on R&D, thus, the capital allocation hypothesis is partly supported. Based on the empirical analysis, we also provide some countermeasures to strengthen the roles of green credit to support the development of energy-saving and environmental enterprises.
Forecasting German Car Sales Using Google Data and Multivariate Models
Fantazzini, Dean; Toktamysova, Zhamal
2015-01-01
Long-term forecasts are of key importance for the car industry due to the lengthy period of time required for the development and production processes. With this in mind, this paper proposes new multivariate models to forecast monthly car sales data using economic variables and Google online search data. An out-of-sample forecasting comparison with forecast horizons up to 2 years ahead was implemented using the monthly sales of ten car brands in Germany for the period from 2001M1 to 2014M6. M...
Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models
DEFF Research Database (Denmark)
Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin
2017-01-01
In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21......, 28 and 34 from all preceding days and provide our interpretation of the results. Results indicate that the dynamic interconnection between environmental conditions and broiler growth can be captured by the model. Furthermore, we found that a comparable forecast can be obtained by using input data...
A numerical forecast model for road meteorology
Meng, Chunlei
2017-05-01
A fine-scale numerical model for road surface parameters prediction (BJ-ROME) is developed based on the Common Land Model. The model is validated using in situ observation data measured by the ROSA road weather stations of Vaisala Company, Finland. BJ-ROME not only takes into account road surface factors, such as imperviousness, relatively low albedo, high heat capacity, and high heat conductivity, but also considers the influence of urban anthropogenic heat, impervious surface evaporation, and urban land-use/land-cover changes. The forecast time span and the update interval of BJ-ROME in vocational operation are 24 and 3 h, respectively. The validation results indicate that BJ-ROME can successfully simulate the diurnal variation of road surface temperature both under clear-sky and rainfall conditions. BJ-ROME can simulate road water and snow depth well if the artificial removing was considered. Road surface energy balance in rainy days is quite different from that in clear-sky conditions. Road evaporation could not be neglected in road surface water cycle research. The results of sensitivity analysis show solar radiation correction coefficient, asphalt depth, and asphalt heat conductivity are important parameters in road interface temperatures simulation. The prediction results could be used as a reference of maintenance decision support system to mitigate the traffic jam and urban water logging especially in large cities.
Brunelli, Alessandro; Salati, Michele; Refai, Majed; Xiumé, Francesco; Rocco, Gaetano; Sabbatini, Armando
2007-09-01
The objectives of this study were to develop a risk-adjusted model to estimate individual postoperative costs after major lung resection and to use it for internal economic audit. Variable and fixed hospital costs were collected for 679 consecutive patients who underwent major lung resection from January 2000 through October 2006 at our unit. Several preoperative variables were used to develop a risk-adjusted econometric model from all patients operated on during the period 2000 through 2003 by a stepwise multiple regression analysis (validated by bootstrap). The model was then used to estimate the postoperative costs in the patients operated on during the 3 subsequent periods (years 2004, 2005, and 2006). Observed and predicted costs were then compared within each period by the Wilcoxon signed rank test. Multiple regression and bootstrap analysis yielded the following model predicting postoperative cost: 11,078 + 1340.3X (age > 70 years) + 1927.8X cardiac comorbidity - 95X ppoFEV1%. No differences between predicted and observed costs were noted in the first 2 periods analyzed (year 2004, $6188.40 vs $6241.40, P = .3; year 2005, $6308.60 vs $6483.60, P = .4), whereas in the most recent period (2006) observed costs were significantly lower than the predicted ones ($3457.30 vs $6162.70, P < .0001). Greater precision in predicting outcome and costs after therapy may assist clinicians in the optimization of clinical pathways and allocation of resources. Our economic model may be used as a methodologic template for economic audit in our specialty and complement more traditional outcome measures in the assessment of performance.
Improving wave forecasting by integrating ensemble modelling and machine learning
O'Donncha, F.; Zhang, Y.; James, S. C.
2017-12-01
Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.
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.
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.
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.
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.
Combined Forecasts from Linear and Nonlinear Time Series Models
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
Combined forecasts from linear and nonlinear time series models
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
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.
Short-term forecasting model for aggregated regional hydropower generation
International Nuclear Information System (INIS)
Monteiro, Claudio; Ramirez-Rosado, Ignacio J.; Fernandez-Jimenez, L. Alfredo
2014-01-01
Highlights: • Original short-term forecasting model for the hourly hydropower generation. • The use of NWP forecasts allows horizons of several days. • New variable to represent the capacity level for generating hydroelectric energy. • The proposed model significantly outperforms the persistence model. - Abstract: This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the “monthly” hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations
Unalaska, Alaska Tsunami Forecast Grids for MOST Model
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...
Myrtle Beach, South Carolina Tsunami Forecast Grids for MOST Model
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)...
Port Angeles, Washington Tsunami Forecast Grids for MOST Model
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)...
Port San Luis, California Tsunami Forecast Grids for MOST Model
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)...
A simulation model for forecasting downhill ski participation
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.
Craig, Alaska Tsunami Forecast Grids for MOST Model
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...
Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model
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...
San Francisco, California Tsunami Forecast Grids for MOST Model
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)...
British Columbia, Canada Tsunami Forecast Grids for MOST Model
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)...
Atka, Alaska Tsunami Forecast Grids for MOST Model
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...
Nantucket, Massachusetts Tsunami Forecast Grids for MOST Model
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)...
Santa Barbara, California Tsunami Forecast Grids for MOST Model
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)...
Cordova, Alaska Tsunami Forecast Grids for MOST Model
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...
Charlotte Amalie, Virgin Islands Tsunami Forecast Grids for MOST Model
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...
Christiansted, Virgin Islands Tsunami Forecast Grids for MOST Model
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)...
Arena Cove, California Tsunami Forecast Grids for MOST Model
National Oceanic and Atmospheric Administration, Department of Commerce — The Arena Cove, California Forecast Grids provides bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST...
Virginia Beach Tsunami Forecast Grids for MOST Model
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)...
Cape Hatteras, North Carolina Tsunami Forecast Grids for MOST Model
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)...
Morehead City, North Carolina Tsunami Forecast Grids for MOST Model
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)...
Crescent City, California Tsunami Forecast Grids for MOST Model
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)...
Point Reyes, California Tsunami Forecast Grids for MOST Model
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)...
Chignik, Alaska Tsunami Forecast Grids for MOST Model
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...
Cash flow forecasting model for nuclear power projects
International Nuclear Information System (INIS)
Liu Wei; Guo Jilin
2002-01-01
Cash flow forecasting is very important for owners and contractors of nuclear power projects to arrange the capital and to decrease the capital cost. The factors related to contractor cash flow forecasting are analyzed and a cash flow forecasting model is presented which is suitable for both contractors and owners. The model is efficiently solved using a cost-schedule data integration scheme described. A program is developed based on the model and verified with real project data. The result indicates that the model is efficient and effective
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.
International Nuclear Information System (INIS)
Lee, M.K.; Kim, S.S.; Moon, K.H.; Song, K.D.; Choi, Y.M.
1997-01-01
It is very important to take a look at the role of nuclear energy within the framework of energy demand and supply when the international environmental regulation is imposed. The main purpose of this study is to estimate the effect of imposition of carbon tax on energy sector in Korea. To do so, an econometric simulation model was developed. The model is composed of not only energy part in detail but also economic activity part in a rather simple manner. To analyze the electric sector in detail, energy block is divided into the electric and the non-electric energy sector. In the electric sector there are four blocks such as demand, conversion efficiency, fuel, and price. Several carbon tax scenarios were assumed to figure out the impacts on such variables as C0 sub 2 emissions, GDP, energy demand and price. After estimating the carbon tax effects, another set of scenario was created in analyzing the possible role of nuclear power for alleviating the impacts from carbon tax. From the results it is found that the national economy is significantly influenced according to which regulation is adopted. If international regulation is imposed on the quantity of total carbon emission, the impact is so severe that Korean economy could not stand alone. Therefore, the economy cannot overcome the impact from the regulation only by the increased share of nuclear. However, if the regulation is imposed on the quantity of carbon emission proportional to population instead of total carbon emission, it would bring definitely better opportunity to the Korean economy. In the latter case, there is room that nuclear can contribute. If the share of nuclear increases up to 60% in 2020 instead of 45%, GDP would rise by 1.9% while the electricity price lower by 46%. The model could be used in other purposed such as studies on the impacts from fuel prices increases, from capital investment costs increases, and so on
Forecasting Analysis of Shanghai Stock Index Based on ARIMA Model
Directory of Open Access Journals (Sweden)
Li Chenggang
2017-01-01
Full Text Available Prediction and analysis of the Shanghai Composite Index is conducive for investors to investing in the stock market, and providing investors with reference. This paper selects Shanghai Composite Index monthly closing price from Jan, 2005 to Oct, 2016 to construct ARIMA model. This paper carries on the forecast of the last three monthly closing price of Shanghai Stock Index that have occurred, and compared it with the actual value, which tests the accuracy and feasibility of the model in the short term Shanghai Stock Index forecast. At last, this paper uses the ARIMA model to forecast the Shanghai Composite Index closing price of the last two months in 2016.
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.
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 substantia...
Network bandwidth utilization forecast model on high bandwidth networks
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2015-03-30
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 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
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.
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)
Modeling and forecasting the supply of oil and gas: a survey of existing approaches
International Nuclear Information System (INIS)
Walls, M.A.
1992-01-01
This paper surveys the literature on empirical oil and gas supply modeling. The models fall into two broad categories: geologic/engineering and econometric. Two types of geologic/engineering models are surveyed - play analysis, or simulation models and discovery process models. A third category of supply models, 'hybrids', which contain features of both econometric and discovery process models are also discussed. Particular attention is paid to whether or not the models have linkages between a dynamic model of producer optimizing behaviour and the factors governing supply of the resource; whether or not expectations of future prices, costs, and other stochastic variables are incorporated; whether the physical characteristics of non-renewable resources are captured; and how well the models perform. The paper concludes that the best path for future research efforts is a hybrid approach where the econometric component is derived from a stochastic dynamic optimization model of exploration behaviour. 51 refs., 3 figs., 1 tab
An econometric model of the U.S. secondary copper industry: Recycling versus disposal
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.
A forecasting model of gaming revenues in Clark County, Nevada
International Nuclear Information System (INIS)
Edwards, B.; Bando, A.; Basset, G.; Rosen, A.; Meenan, C.; Carlson, J.
1992-01-01
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, and 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
A forecasting model of gaming revenues in Clark County, Nevada
International Nuclear Information System (INIS)
Edwards, B.; Bando, A.; Bassett, G.; Rosen, A.; Carlson, J.; Meenan, C.
1992-01-01
This paper describes the Western Area Gaining 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
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.
Medium Range Forecast (MRF) and Nested Grid Model (NGM)
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...
Evaluation and Application of the Weather Research and Forecast Model
National Research Council Canada - National Science Library
Passner, Jeffrey E
2007-01-01
... by the U.S. Army Research Laboratory (ARL) to determine how accurate and robust the model is under a variety of meteorological conditions, with an emphasis on fine resolution, short-range forecasts in complex terrain...
Human-model hybrid Korean air quality forecasting system.
Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun
2016-09-01
The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the
Modelling and forecasting Turkish residential electricity demand
International Nuclear Information System (INIS)
Dilaver, Zafer; Hunt, Lester C
2011-01-01
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.
Compilation Of An Econometric Human Resource Efficiency Model For Project Management Best Practices
G. van Zyl; 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...
Econometric analysis and energy substitution
International Nuclear Information System (INIS)
Phillips, G.J.
1981-09-01
As part of its long-term assessment of new applications for nuclear energy, AECL is becoming acquainted with the techniques of mathematical modelling as used in the areas of energy and economics. Early in 1980, a contract was arranged with DataMetrics Limited of Calgary to prepare an econometric model of the manufacturing sector for Ontario, and to provide AECL with all the information necessary to understand the theory, derivation, and use of the model. This report summarizes the results of this exercise
Are traditional forecasting models suitable for hotels in Italian cities?
ELLERO, Andrea; PELLEGRINI, Paola
2014-01-01
The aim of this paper is to assess the performance of different widely-adopted models to forecast Italian hotel occupancy. In particular, the paper tests the different models for forecasting the demand in hotels located in urban areas, which typically experience both business and leisure demand, and whose demand is often affected by the presence of special events in the hotels themselves, or in their neighborhood.
Inventory model using bayesian dynamic linear model for demand forecasting
Directory of Open Access Journals (Sweden)
Marisol Valencia-Cárdenas
2014-12-01
Full Text Available An important factor of manufacturing process is the inventory management of terminated product. Constantly, industry is looking for better alternatives to establish an adequate plan of production and stored quantities, with optimal cost, getting quantities in a time horizon, which permits to define resources and logistics with anticipation, needed to distribute products on time. Total absence of historical data, required by many statistical models to forecast, demands the search for other kind of accurate techniques. This work presents an alternative that not only permits to forecast, in an adjusted way, but also, to provide optimal quantities to produce and store with an optimal cost, using Bayesian statistics. The proposal is illustrated with real data. Palabras clave: estadística bayesiana, optimización, modelo de inventarios, modelo lineal dinámico bayesiano. Keywords: Bayesian statistics, opti
Directory of Open Access Journals (Sweden)
Yuqi Dong
2016-12-01
Full Text Available Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.
Evaluation of Loss Due to Storm Surge Disasters in China Based on Econometric Model Groups.
Jin, Xue; Shi, Xiaoxia; Gao, Jintian; Xu, Tongbin; Yin, Kedong
2018-03-27
Storm surge has become an important factor restricting the economic and social development of China's coastal regions. In order to improve the scientific judgment of future storm surge damage, a method of model groups is proposed to refine the evaluation of the loss due to storm surges. Due to the relative dispersion and poor regularity of the natural property data (login center air pressure, maximum wind speed, maximum storm water, super warning water level, etc.), storm surge disaster is divided based on eight kinds of storm surge disaster grade division methods combined with storm surge water, hypervigilance tide level, and disaster loss. The storm surge disaster loss measurement model groups consist of eight equations, and six major modules are constructed: storm surge disaster in agricultural loss, fishery loss, human resource loss, engineering facility loss, living facility loss, and direct economic loss. Finally, the support vector machine (SVM) model is used to evaluate the loss and the intra-sample prediction. It is indicated that the equations of the model groups can reflect in detail the relationship between the damage of storm surges and other related variables. Based on a comparison of the original value and the predicted value error, the model groups pass the test, providing scientific support and a decision basis for the early layout of disaster prevention and mitigation.
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.
Spatio-temporal modeling for real-time ozone forecasting.
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.
Coastal and Riverine Flood Forecast Model powered by ADCIRC
Khalid, A.; Ferreira, C.
2017-12-01
Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which
An Econometric Examination of the Behavioral Perspective Model in the Context of Norwegian Retailing
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…
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.
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.
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.
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.
Caruso, Geoffrey; Cavailhès, Jean; Peeters, Dominique; Thomas, Isabelle; Frankhauser, Pierre; Vuidel, Gilles
2015-01-01
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. PMID:26958606
The meat market: a dea international perspective and an econometric behavioral model for Brazil
Directory of Open Access Journals (Sweden)
Geraldo da Silva e Souza
2014-09-01
Full Text Available We describe the relative participation of the Brazilian meat market (beef, pork and chicken in total agribusiness exports and in total country exports. An analysis of the world meat market is carried out from the point of view of the values of consumption, production, exports and imports. A DEA (data envelopment analysis approach is then used to generate classifications of the importance of countries in the meat world market, and the insertion of Brazil into this market is viewed from these perspectives. A partial equilibrium model for the meat market is fitted to Brazilian data by a three-stage least squares procedure. The model is consistent with the data and is used for simulation purposes. In this context, we investigate the joint and separate effects of changes in the corn price and in the exchange rate on the market of endogenous variables, ceteris paribus.
Comparison of various models on cancer rate and forecasting ...
African Journals Online (AJOL)
In this research work, three models were identified; linear regression model, exponential growth model and the quadratic trend model and the results of the work compared. Data collected from Niger State Hospital Management Board was used for the forecast and the result revealed that the quadratic trend model gave the ...
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.
Meteoroid Environment Modeling: the Meteoroid Engineering Model and Shower Forecasting
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
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.
A Modeling Framework for Improved Agricultural Water Supply Forecasting
Leavesley, G. H.; David, O.; Garen, D. C.; Lea, J.; Marron, J. K.; Pagano, T. C.; Perkins, T. R.; Strobel, M. L.
2008-12-01
The National Water and Climate Center (NWCC) of the USDA Natural Resources Conservation Service is moving to augment seasonal, regression-equation based water supply forecasts with distributed-parameter, physical process models enabling daily, weekly, and seasonal forecasting using an Ensemble Streamflow Prediction (ESP) methodology. This effort involves the development and implementation of a modeling framework, and associated models and tools, to provide timely forecasts for use by the agricultural community in the western United States where snowmelt is a major source of water supply. The framework selected to support this integration is the USDA Object Modeling System (OMS). OMS is a Java-based modular modeling framework for model development, testing, and deployment. It consists of a library of stand-alone science, control, and database components (modules), and a means to assemble selected components into a modeling package that is customized to the problem, data constraints, and scale of application. The framework is supported by utility modules that provide a variety of data management, land unit delineation and parameterization, sensitivity analysis, calibration, statistical analysis, and visualization capabilities. OMS uses an open source software approach to enable all members of the scientific community to collaboratively work on addressing the many complex issues associated with the design, development, and application of distributed hydrological and environmental models. A long-term goal in the development of these water-supply forecasting capabilities is the implementation of an ensemble modeling approach. This would provide forecasts using the results of multiple hydrologic models run on each basin.
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.
Proposal of a congestion control technique in LAN networks using an econometric model ARIMA
Directory of Open Access Journals (Sweden)
Joaquín F Sánchez
2017-01-01
Full Text Available Hasty software development can produce immediate implementations with source code unnecessarily complex and hardly readable. These small kinds of software decay generate a technical debt that could be big enough to seriously affect future maintenance activities. This work presents an analysis technique for identifying architectural technical debt related to non-uniformity of naming patterns; the technique is based on term frequency over package hierarchies. The proposal has been evaluated on projects of two popular organizations, Apache and Eclipse. The results have shown that most of the projects have frequent occurrences of the proposed naming patterns, and using a graph model and aggregated data could enable the elaboration of simple queries for debt identification. The technique has features that favor its applicability on emergent architectures and agile software development.
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...
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.
Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques
Monteiro, Claudio; Fernandez-Jimenez, L. Alfredo; Ramirez-Rosado, Ignacio J.; Muñoz-Jimenez, Andres; Lara-Santillan, Pedro M.
2013-01-01
We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adj...
Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling.
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.
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.
Aerosol Radiative Forcing and Weather Forecasts in the ECMWF Model
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
Comparison of various models on cancer rate and forecasting ...
African Journals Online (AJOL)
ADOWIE PERE
ABSTRACT: In this research work, three models were identified; linear regression model, exponential growth model and the quadratic trend model and the results of the work compared. Data collected from Niger State Hospital. Management Board was used for the forecast and the result revealed that the quadratic trend ...
forecasting with nonlinear time series model: a monte-carlo ...
African Journals Online (AJOL)
PUBLICATIONS1
with nonlinear time series model by comparing the RMSE with the traditional bootstrap and. Monte-Carlo method of forecasting. We use the logistic smooth transition autoregressive. (LSTAR) model as a case study. We first consider a linear model called the AR. (p) model of order p which satisfies the follow- ing linear ...
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.
Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model
Directory of Open Access Journals (Sweden)
Marko Intihar
2017-11-01
Full Text Available The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020. Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.
Functional dynamic factor models with application to yield curve forecasting
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.
International Nuclear Information System (INIS)
Liu, Xiuli; Moreno, Blanca; García, Ana Salomé
2016-01-01
A combined forecast of Grey forecasting method and neural network back propagation model, which is called Grey Neural Network and Input-Output Combined Forecasting Model (GNF-IO model), is proposed. A real case of energy consumption forecast is used to validate the effectiveness of the proposed model. The GNF-IO model predicts coal, crude oil, natural gas, renewable and nuclear primary energy consumption volumes by Spain's 36 sub-sectors from 2010 to 2015 according to three different GDP growth scenarios (optimistic, baseline and pessimistic). Model test shows that the proposed model has higher simulation and forecasting accuracy on energy consumption than Grey models separately and other combination methods. The forecasts indicate that the primary energies as coal, crude oil and natural gas will represent on average the 83.6% percent of the total of primary energy consumption, raising concerns about security of supply and energy cost and adding risk for some industrial production processes. Thus, Spanish industry must speed up its transition to an energy-efficiency economy, achieving a cost reduction and increase in the level of self-supply. - Highlights: • Forecasting System Using Grey Models combined with Input-Output Models is proposed. • Primary energy consumption in Spain is used to validate the model. • The grey-based combined model has good forecasting performance. • Natural gas will represent the majority of the total of primary energy consumption. • Concerns about security of supply, energy cost and industry competitiveness are raised.
Short term forecasting of petroleum product demand in France
International Nuclear Information System (INIS)
Cadren, M.
1998-01-01
The analysis of petroleum product demand became a privileged thrust of research following the modifications in terms of structure and level of the petroleum markets since eighties. The greatest importance to econometrics models of Energy demand, joint works about nonstationary data, explained the development of error-correction models and the co-integration. In this context, the short term econometrics modelling of petroleum product demand does not only focus on forecasts but also on the measure of the gain acquired from using error-correction techniques and co-integration. It's filling to take the influence of technical improvement and environment pressures into account in econometrics modelling of petroleum products demand. The first part presents the evolution of Energy Demand in France and more particularly the petroleum product demand since 1986. The objective is to determine the main characteristics of each product, which will help us to analyse and validate the econometrics models. The second part focus on the recent developments in times series modelling. We study the problem of nonstationary data and expose different unit root tests. We examine the main approaches to univariate and multivariate modelling with nonstationary data and distinguish the forecasts of the latter's. The third part is intended to applications; its objective is to illustrate the theoretic developments of the second part with a comparison between the performances of different approaches (approach Box and Jenkins, Johansen approach's and structural approach). The models will be applied to the main French petroleum market. The observed asymmetrical demand behaviour is also considered. (author)
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.
Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting
Directory of Open Access Journals (Sweden)
Bijay Neupane
2017-01-01
Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.
Earthquake forecast models for Italy based on the RI algorithm
Directory of Open Access Journals (Sweden)
Kazuyoshi Z. Nanjo
2010-11-01
Full Text Available This study provides an overview of relative-intensity (RI-based earthquake forecast models that have been submitted for the 5-year and 10-year testing classes and the 3-month class of the Italian experiment within the Collaboratory for the Study of Earthquake Predictability (CSEP. The RI algorithm starts as a binary forecast system based on the working assumption that future large earthquakes are considered likely to occur at sites of higher seismic activity in the past. The measure of RI is the simply counting of the number of past earthquakes, which is known as the RI of seismicity. To improve the RI forecast performance, we first expand the RI algorithm to become part of a general class of smoothed seismicity models. We then convert the RI representation from a binary system into a testable CSEP model that forecasts the numbers of earthquakes for the predefined magnitudes. Our parameter tuning for the CSEP models is based on the past seismicity. The final submission is a set of two numerical data files that were created by tuned 5-year and 10-year models and an executable computer code of a tuned 3-month model, to examine which testing class is more meaningful in terms of the RI hypothesis. The main purpose of our participation is to better understand the importance (or lack of importance of RI of seismicity for earthquake forecastability.
A Hidden Markov Model for avalanche forecasting on Chowkibal ...
Indian Academy of Sciences (India)
... different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008–2009, 2009–2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and ...
Validation of Model Forecasts of the Ambient Solar Wind
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.
Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model
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...
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.
Air Quality Forecasts Using the NASA GEOS Model
Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua;
2018-01-01
We provide an introduction to a new high-resolution (0.25 degree) global composition forecast produced by NASA's Global Modeling and Assimilation office. The NASA Goddard Earth Observing System version 5 (GEOS-5) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (25 km). Previously, this combination of detailed chemistry and resolution was only provided by regional models. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 11) to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest resolution compared to current, publically-available global composition forecasts. Evaluation and validation of modeled trace gases and aerosols compared to surface and satellite observations will be presented for constituents relative to health air quality standards. Comparisons of modeled trace gases and aerosols against satellite observations show that the model produces realistic concentrations of atmospheric constituents in the free troposphere. Model comparisons against surface observations highlight the model's capability to capture the diurnal variability of air pollutants under a variety of meteorological conditions. The GEOS-5 composition forecasting system offers a new tool for scientists and the public health community, and is being developed jointly with several government and non-profit partners. Potential applications include air quality warnings, flight campaign planning and exposure studies using the archived analysis fields.
The rationality of EIA forecasts under symmetric and asymmetric loss
International Nuclear Information System (INIS)
Auffhammer, Maximilian
2007-01-01
The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)
Directory of Open Access Journals (Sweden)
Acacia S. Pepler
2015-09-01
Full Text Available Dynamical models are now widely used to provide forecasts of above or below average seasonal mean temperatures and precipitation, with growing interest in their ability to forecast climate extremes on a seasonal time scale. This study assesses the skill of the ENSEMBLES multi-model ensemble to forecast the 90th and 10th percentiles of both seasonal temperature and precipitation, using a number of metrics of ‘extremeness’. Skill is generally similar or slightly lower to that for seasonal means, with skill strongly influenced by the El Niño-Southern Oscillation. As documented in previous studies, much of the skill in forecasting extremes can be related to skill in forecasting the seasonal mean value, with skill for extremes generally lower although still significant. Despite this, little relationship is found between the skill of forecasting the upper and lower tails of the distribution of daily values.
Improving the Performance of Water Demand Forecasting Models by Using Weather Input
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
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)
Automation of energy demand forecasting
Siddique, Sanzad
Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.
International Nuclear Information System (INIS)
Carlevaro, F.; Bertholet, J.L.; Chaze, J.P.; Taffe, P.
1992-01-01
It is well-known that road transport is the economic sector most responsible for the growing consumption of fossil fuels in developed countries and, as a consequence, of carbon dioxide emissions largely accountable of the greenhouse effect. Recently, the Swiss Federal Council, as many other European governments, has undertaken to set up an energy policy aiming at reducing CO 2 emissions according to the so-called Toronto targets. Among the different measures that can be used to achieve this objective, special attention has been devoted to the carbon tax as an economic incentive to reduce fossil fuel consumption. Considering motor fuels, such a taxation can lead to a decrease in fuel demand through many mechanisms. Conventional econometric models of energy demand, even when they allow to distinguish short-run from long-run responses of consumption to changing economic conditions, do not consider this engineering description of energy demand. Therefore, they fail to capture the important distinction between demand and supply reaction of energy consumption to energy taxation. The authors present an econometric model built according to an engineering logic which takes into account three possible impacts of a motor fuel tax: on the stock of cars, on their average specific consumption and on their average intensity of use. 5 refs., 2 figs
Schuurmans, J.M.
2008-01-01
Keywords: hydrology, models, soil moisture, rainfall, radar, rain gauge, remote sensing, evapotranspiration, forecasting, numerical weather prediction, Netherlands, Langbroekerwetering, Lopikerwaard. Computer simulation models are an important tool for hydrologists. With these models they can
International Nuclear Information System (INIS)
Estomin, S.L.; Beach, J.E.
1990-10-01
The two-volume report presents the results of an econometric forecast of peak load and electric power demands for the Delmarva Power and Light Company (DP ampersand L) through the year 2008. Separate sets of models were estimated for the three jurisdictions served by DP ampersand L: Delaware, Maryland and Virginia. For both Delaware and Maryland, econometric equations were estimated for residential, commercial, industrial, and streetlighting sales. For Virginia, equations were estimated for residential, commercial plus industrial, and streetlighting sales; separate industrial and commercial equations were not estimated for Virginia due to the relatively small size of DP ampersand L's Virginia Industrial load. Wholesale sales were econometrically estimated for the DP ampersand L system as a whole. In addition to the energy sales models, an econometric model of annual (summer) peak demand was estimated for the Company
Machine learning based switching model for electricity load forecasting
International Nuclear Information System (INIS)
Fan Shu; Chen Luonan; Lee, Weijen
2008-01-01
In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma
Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
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
California demand and supply of crude oil: An econometric analysis with projections to 2000
International Nuclear Information System (INIS)
Ibegbulam, B.N.
1991-01-01
Forecast of California domestic crude oil supply requires the forecasts of California crude oil production and supply from Alaska. Future California crude oil production is forecast with an econometric model that postulates production as a function of reserves and reserves as a function of crude oil prices and exploration and development costs. Future supplies from Alaska are obtained by subtracting forecasts of Alaskan crude oil demand and shipments to the States of Hawaii, Oregon, and Washington from Alaskan North Slope crude oil production forecasts. A two-stage process was used to forecast future California crude oil demand. In the first stage, the demand for refined crude oil products was predicted with a single-equation double logarithmic rational-expectations dynamic model. In the second stage, the total demands obtained in the first stage were converted into a crude oil equivalent. It was found that the current surplus of domestic crude oil in California will end in 1994. Thereafter, California crude oil imports will sharply increase
Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques
Directory of Open Access Journals (Sweden)
Fakhri J. Hasanov
2016-12-01
Full Text Available Policymakers in developing and transitional economies require sound models to: (i understand the drivers of rapidly growing energy consumption and (ii produce forecasts of future energy demand. This paper attempts to model electricity demand in Azerbaijan and provide future forecast scenarios—as far as we are aware this is the first such attempt for Azerbaijan using a comprehensive modelling framework. Electricity consumption increased and decreased considerably in Azerbaijan from 1995 to 2013 (the period used for the empirical analysis—it increased on average by about 4% per annum from 1995 to 2006 but decreased by about 4½% per annum from 2006 to 2010 and increased thereafter. It is therefore vital that Azerbaijani planners and policymakers understand what drives electricity demand and be able to forecast how it will grow in order to plan for future power production. However, modeling electricity demand for such a country has many challenges. Azerbaijan is rich in energy resources, consequently GDP is heavily influenced by oil prices; hence, real non-oil GDP is employed as the activity driver in this research (unlike almost all previous aggregate energy demand studies. Moreover, electricity prices are administered rather than market driven. Therefore, different cointegration and error correction techniques are employed to estimate a number of per capita electricity demand models for Azerbaijan, which are used to produce forecast scenarios for up to 2025. The resulting estimated models (in terms of coefficients, etc. and forecasts of electricity demand for Azerbaijan in 2025 prove to be very similar; with the Business as Usual forecast ranging from about of 19½ to 21 TWh.
Essays in financial econometrics
Αντύπας, Αντώνιος Τ.
2012-01-01
The present doctoral thesis covers different aspects in the financial econometrics area. In particular, the research focuses on the heterogeneous agents in the market (rational and behavioural), the performance measures related to this type of agents and, more generally, the asset evaluation within a portfolio selection framework. Further, the time varying dependence among the financial markets is also considered. In general, the financial markets represent one of the main indicators fo...
Lake Michigan lake trout PCB model forecast post audit
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...
Mountain range specific analog weather forecast model for ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 117; Issue 5. Mountain range specific ... Mountain range speciﬁc analog weather forecast model is developed utilizing surface weather observations of reference stations in each mountain range in northwest Himalaya (NW-Himalaya).The model searches past ...
Forecasting flood-prone areas using Shannon's entropy model
Indian Academy of Sciences (India)
Then, flood susceptibility forecasting map was provided and model accuracy evaluation was conducted using ROC curve and 30% flooding areas express good precision of the model (73.5%) for the study area. ... Department of Watershed Management Engineering, Faculty of Agriculture, Lorestan University, Lorestan, Iran.
Levenberg-Marquardt Recurrent Networks for Long-Term Electricity Peak Load Forecasting
Charles O.P. Marpaung; Weerakorn Ongsakul; Yusak Tanoto
2011-01-01
Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt learning algorithm is proposed to forecast annual peak load of Java-Madura-Bali interconnection for 2...
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.
Ionospheric scintillation forecasting model based on NN-PSO technique
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.
Applied Spatial Econometrics : Raising the Bar
Elhorst, J. Paul
This paper places the key issues and implications of the new 'introductory' book on spatial econometrics by James LeSage & Kelley Pace (2009) in a broader perspective: the argument in favour of the spatial Durbin model, the use of indirect effects as a more valid basis for testing whether spatial
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.
Intercomparison of mesoscale meteorological models for precipitation forecasting
Directory of Open Access Journals (Sweden)
E. Richard
2003-01-01
Full Text Available In the framework of the RAPHAEL EU project, a series of past heavy precipitation events has been simulated with different meteorological models. Rainfall hindcasts and forecasts have been produced by four models in use at various meteorological services or research centres of Italy, Canada, France and Switzerland. The paper is focused on the comparison of the computed precipitation fields with the available surface observations. The comparison is carried out for three meteorological situations which lead to severe flashflood over the Toce-Ticino catchment in Italy (6599 km2 or the Ammer catchment (709 km2 in Germany. The results show that all four models reproduced the occurrence of these heavy precipitation events. The accuracy of the computed precipitation appears to be more case-dependent than model-dependent. The sensitivity of the computed rainfall to the boundary conditions (hindcast v. forecast was found to be rather weak, indicating that a flood forecasting system based upon a numerical meteo-hydrological simulation could be feasible in an operational context. Keywords: meteorological models, precipitation forecast
Electricity demand forecasting techniques
International Nuclear Information System (INIS)
Gnanalingam, K.
1994-01-01
Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important
Comparison of Conventional and ANN Models for River Flow Forecasting
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.
Two empirical models for short-term forecast of Kp
Luo, B.; Liu, S.; Gong, J.
2017-03-01
In this paper, two empirical models are developed for short-term forecast of the Kp index, taking advantage of solar wind-magnetosphere coupling functions proposed by the research community. Both models are based on the data for years 1995 to 2004. Model 1 mainly uses solar wind parameters as the inputs, while model 2 also utilizes the previous measured Kp value. Finally, model 1 predicts Kp with a linear correlation coefficient (r) of 0.91, a prediction efficiency (PE) of 0.81, and a root-mean-square (RMS) error of 0.59. Model 2 gives an r of 0.92, a PE of 0.84, and an RMS error of 0.57. The two models are validated through out-of-sample test for years 2005 to 2013, which also yields high forecast accuracy. Unlike in the other models reported in the literature, we are taking the response time of the magnetosphere to external solar wind at the Earth explicitly in the modeling. Statistically, the time delay in the models turns out to be about 30 min. By introducing this term, both the accuracy and lead time of the model forecast are improved. Through verification and validation, the models can be used in operational geomagnetic storm warnings with reliable performance.
Temperature sensitivity of a numerical pollen forecast model
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.
Short-term integrated forecasting system : 1993 model documentation report
1993-12-01
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 U.S. Energy Department (DOE) developed the STIFS model to generate shor...
A stochastic model for forecast consumption in master scheduling
Weeda, P.J.; Weeda, P.J.
1994-01-01
This paper describes a stochastic model for the reduction of the initial forecast in the Master Schedule (MS) of an MRP system during progress of time by the acceptance of customer orders. Results are given for the expectation and variance of the number of yet unknown deliveries as a function of
A Model for Understanding Management Manpower: Forecasting and Planning
Deckard, Noble S.; Lessey, Kenneth W.
1975-01-01
The authors, realizing the importance of continuous organizational reappraisal of manpower needs and strengths, have developed a model based on supply of management manpower and demand for management manpower. Without a manpower forecasting/planning program, the future needs of the organization are reduced to guesswork. (EA)
Inflation, Forecast Intervals and Long Memory Regression Models
C.S. Bos (Charles); Ph.H.B.F. Franses (Philip Hans); M. Ooms (Marius)
2001-01-01
textabstractWe examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading
Inflation, Forecast Intervals and Long Memory Regression Models
Ooms, M.; Bos, C.S.; Franses, P.H.
2003-01-01
We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators
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.
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
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)
An improved market penetration model for wind energy technology forecasting
International Nuclear Information System (INIS)
Lund, P.D.
1995-01-01
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)
NA
2005-01-01
This paper presents a study of errors in forecasting the population of Metropolitan Statistical Areas and the Primary MSAs of Consolidated Metropolitan Statistical Areas and New England MAs. The forecasts are for the year 2000 and are based on a semi-structural model estimated by Mills and Lubelle using 1970 to 1990 census data on population, employment and relative real wages. This model allows the testing of regional effects on population and employment growth. The year 2000 forecasts are f...
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.
Regional forecasting with global atmospheric models
International Nuclear Information System (INIS)
Crowley, T.J.; North, G.R.; Smith, N.R.
1994-05-01
The scope of the report is to present the results of the fourth year's work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals
Using phenomenological models for forecasting the 2015 Ebola challenge
Directory of Open Access Journals (Sweden)
Bruce Pell
2018-03-01
Full Text Available Background: The rising number of novel pathogens threatening the human population has motivated the application of mathematical modeling for forecasting the trajectory and size of epidemics. Materials and methods: We summarize the real-time forecasting results of the logistic equation during the 2015 Ebola challenge focused on predicting synthetic data derived from a detailed individual-based model of Ebola transmission dynamics and control. We also carry out a post-challenge comparison of two simple phenomenological models. In particular, we systematically compare the logistic growth model and a recently introduced generalized Richards model (GRM that captures a range of early epidemic growth profiles ranging from sub-exponential to exponential growth. Specifically, we assess the performance of each model for estimating the reproduction number, generate short-term forecasts of the epidemic trajectory, and predict the final epidemic size. Results: During the challenge the logistic equation consistently underestimated the final epidemic size, peak timing and the number of cases at peak timing with an average mean absolute percentage error (MAPE of 0.49, 0.36 and 0.40, respectively. Post-challenge, the GRM which has the flexibility to reproduce a range of epidemic growth profiles ranging from early sub-exponential to exponential growth dynamics outperformed the logistic growth model in ascertaining the final epidemic size as more incidence data was made available, while the logistic model underestimated the final epidemic even with an increasing amount of data of the evolving epidemic. Incidence forecasts provided by the generalized Richards model performed better across all scenarios and time points than the logistic growth model with mean RMS decreasing from 78.00 (logistic to 60.80 (GRM. Both models provided reasonable predictions of the effective reproduction number, but the GRM slightly outperformed the logistic growth model with a MAPE of 0
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.
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.
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)
International Nuclear Information System (INIS)
Ogawa, Takeshi; Kayano, Mitsunaga; Kikuchi, Hideo; Abe, Takeo; Saga, Kyoji
1995-01-01
In Environmental Radioactivity Research Institute, the verification and investigation of the wind velocity field forecast model 'EXPRESS-1' have been carried out since 1991. In fiscal year 1994, as the general analysis, the validity of weather observation data, the local features of wind field, and the validity of the positions of monitoring stations were investigated. The EXPRESS which adopted 500 m mesh so far was improved to 250 m mesh, and the heightening of forecast accuracy was examined, and the comparison with another wind velocity field forecast model 'SPEEDI' was carried out. As the results, there are the places where the correlation with other points of measurement is high and low, and it was found that for the forecast of wind velocity field, by excluding the data of the points with low correlation or installing simplified observation stations to take their data in, the forecast accuracy is improved. The outline of the investigation, the general analysis of weather observation data and the improvements of wind velocity field forecast model and forecast accuracy are reported. (K.I.)
The use of HBV model for flash flood forecasting
Directory of Open Access Journals (Sweden)
M. Kobold
2006-01-01
Full Text Available The standard conceptual HBV model was originally developed with daily data and is normally operated on daily time step. But many floods in Slovenia are usually flash floods as result of intense frontal precipitation combined with orographic enhancement. Peak discharges are maintained only for hours or even minutes. To use the HBV model for flash flood forecasting, the version of HBV-96 has been applied on the catchment with complex topography with the time step of one hour. The recording raingauges giving hourly values of precipitation have been taken in calibration of the model. The uncertainty of simulated runoff is mainly the result of precipitation uncertainty associated with the mean areal precipitation and is higher for mountainous catchments. Therefore the influence of number of raingauges used to derive the areal precipitation by the method of Thiessen polygons was investigated. The quantification of hydrological uncertainty has been performed by analysis of sensitivity of the HBV model to error in precipitation input. The results show that an error of 10% in the amount of precipitation causes an error of 17% in the peak of flood wave. The polynomial equations showing the relationship between the errors in rainfall amounts and peak discharges were derived for two water stations on the Savinja catchment. Simulated discharges of half-yearly runs demonstrate the applicability of the HBV model for flash flood forecasting using the mesoscale meteorological forecasts of ALADIN/SI model as input precipitation data.
A forecast comparison of volatility models
DEFF Research Database (Denmark)
Hansen, Peter Reinhard; Lunde, Asger
2005-01-01
We compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM-$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outpe...
Model Uncertainty and Exchange Rate Forecasting
Kouwenberg, R.; Markiewicz, A.; Verhoeks, R.; Zwinkels, R.C.J.
2017-01-01
Exchange rate models with uncertain and incomplete information predict that investors focus on a small set of fundamentals that changes frequently over time. We design a model selection rule that captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample tests show
Regional forecasting with global atmospheric models
International Nuclear Information System (INIS)
Crowley, T.J.; North, G.R.; Smith, N.R.
1994-05-01
This report was prepared by the Applied Research Corporation (ARC), College Station, Texas, under subcontract to Pacific Northwest Laboratory (PNL) as part of a global climate studies task. The task supports site characterization work required for the selection of a potential high-level nuclear waste repository and is part of the Performance Assessment Scientific Support (PASS) Program at PNL. The work is under the overall direction of the Office of Civilian Radioactive Waste Management (OCRWM), US Department of Energy Headquarters, Washington, DC. The scope of the report is to present the results of the third year's work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain several studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals
National Research Council Canada - National Science Library
Passner, Jeffrey E
2008-01-01
...) as well as for longer-range forecasting support. The model utilized to investigate fine-scale weather processes, the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW...
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.
Daily reservoir inflow forecasting combining QPF into ANNs model
Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian
2009-01-01
Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.
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...
Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models
Directory of Open Access Journals (Sweden)
Shelton Peiris
2017-12-01
Full Text Available This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV components in order to develop the General Long Memory SV (GLMSV model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.
Uncertainty calculation in transport models and forecasts
DEFF Research Database (Denmark)
Manzo, Stefano; Prato, Carlo Giacomo
. Forthcoming: European Journal of Transport and Infrastructure Research, 15-3, 64-72. 4 The last paper4 examined uncertainty in the spatial composition of residence and workplace locations in the Danish National Transport Model. Despite the evidence that spatial structure influences travel behaviour...... to increase the quality of the decision process and to develop robust or adaptive plans. In fact, project evaluation processes that do not take into account model uncertainty produce not fully informative and potentially misleading results so increasing the risk inherent to the decision to be taken...
Forecasting characteristic earthquakes in a minimalist model
DEFF Research Database (Denmark)
Vázquez-Prada, M.; Pacheco, A.; González, Á.
2003-01-01
-dimensional numerical exploration of the loss function. This first strategy is then refined by considering a classification of the seismic cycles of the model according to the presence, or not, of some factors related to the seismicity observed in the cycle. These factors, statistically speaking, enlarge or shorten...
An efficient and simplified model for forecasting using SRM
International Nuclear Information System (INIS)
Asif, H.M.; Hyat, M.F.; Ahmad, T.
2014-01-01
Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory), SRM (Structural Risk Minimization )Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression) for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building. (author)
An Efficient and Simplified Model for Forecasting using SRM
Directory of Open Access Journals (Sweden)
Hafiz Muhammad Shahzad Asif
2014-01-01
Full Text Available Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines, provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory, SRM (Structural Risk Minimization Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building
A short-range objective nocturnal temperature forecasting model
Sutherland, R. A.
1980-01-01
A relatively simple, objective, nocturnal temperature forecasting model suitable for freezing and near-freezing conditions has been designed so that a user, presumably a weather forecaster, can put in standard meteorological data at a particular location and receive an hour-by-hour prediction of surface and air temperatures for that location for an entire night. The user has the option of putting in his own estimates of wind speeds and background sky radiation which are treated as independent variables. An analysis of 141 test runs show that 57.4% of the time the model predicts to within 1 C for the best cases and to within 3 C for 98.0% of all cases.
MODELLING CHALLENGES TO FORECAST URBAN GOODS DEMAND FOR RAIL
Directory of Open Access Journals (Sweden)
Antonio COMI
2015-12-01
Full Text Available This paper explores the new research challenges for forecasting urban goods demand by rail. In fact, the growing interest to find urban logistics solutions for improving city sustainability and liveability, mainly due to the reduction of urban road accessibility and environmental constraints, has pushed to explore solutions alternative to the road. Multimodal urban logistics, based on the use of railway, seem an interesting alternative solution, but it remained mainly at conceptual level. Few studies have explored the factors, that push actors to find competitive such a system with respect to the road, and modelling framework for forecasting the relative demand. Therefore, paper reviews the current literature, investigates the factors involved in choosing such a mode, and finally, recalls a recent modelling framework and hence proposes some advancements that allow to point out the rail transport alternative.
Modeling and forecasting persistent financial durations
Czech Academy of Sciences Publication Activity Database
Žikeš, F.; Baruník, Jozef; Shenai, N.
2017-01-01
Roč. 36, č. 10 (2017), s. 1081-1110 ISSN 0747-4938 R&D Projects: GA ČR GA13-32263S Institutional support: RVO:67985556 Keywords : Long memory * multifractal models * price durations * realized volatility * whittle estimation Subject RIV: AH - Economics OBOR OECD: Finance Impact factor: 1.333, year: 2016 http://library.utia.cas.cz/separaty/2017/E/barunik-0478483.pdf
A Bayesian Technique for Selecting a Linear Forecasting Model
Ramona L. Trader
1983-01-01
The specification of a forecasting model is considered in the context of linear multiple regression. Several potential predictor variables are available, but some of them convey little information about the dependent variable which is to be predicted. A technique for selecting the "best" set of predictors which takes into account the inherent uncertainty in prediction is detailed. In addition to current data, there is often substantial expert opinion available which is relevant to the forecas...
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Maui-Oahu
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,...
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....
Weather Research and Forecasting (WRF) Regional Atmospheric Model: Main Hawaiian Islands
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)...
State-level electricity demand forecasting model. [For 1980, 1985, 1990
Energy Technology Data Exchange (ETDEWEB)
Nguyen, H. D.
1978-01-01
This note briefly describes the Oak Ridge National Laboratory (ORNL) state-level electricity demand (SLED) forecasting model developed for the Nuclear Regulatory Commission. Specifically, the note presents (1) the special features of the model, (2) the methodology used to forecast electricity demand, and (3) forecasts of electricity demand and average price by sector for 15 states for 1980, 1985, 1990.
Do regional weather models contribute to better wind power forecasts? A few Norwegian case studies
DEFF Research Database (Denmark)
Bremnes, John Bjørnar; Giebel, Gregor
2017-01-01
In most operational wind power forecasting systems statistical methods are applied to map wind forecasts from numerical weather prediction (NWP) models into wind power forecasts. NWP models are complex mathematical models of the atmosphere that divide the earth’s surface into a grid. The spatial...... resolution of this grid determines how accurate meteorological processes can be modeled and thereby also limits forecast quality. In this study, two global and four regional operational NWP models with spatial horizontal resolutions ranging from 1 to 32 km were applied to make wind power forecasts up to 66...
Heap Leaching: Modelling and Forecasting Using CFD Technology
Directory of Open Access Journals (Sweden)
Diane McBride
2018-01-01
Full Text Available Heap leach operations typically employ some form of modelling and forecasting tools to predict cash flow margins and project viability. However, these vary from simple spreadsheets to phenomenological models, with more complex models not commonly employed as they require the greatest amount of time and effort. Yet, accurate production modelling and forecasting are essential for managing production and potentially critical for successful operation of a complex heap, time and effort spent in setting up modelling tools initially may increase profitability in the long term. A brief overview of various modelling approaches is presented, but this paper focuses on the capabilities of a computational fluid dynamics (CFD model. Advances in computational capability allow for complex CFD models, coupled with leach kinetic models, to be applied to complex ore bodies. In this paper a comprehensive hydrodynamic CFD model is described and applied to chalcopyrite dissolution under heap operating conditions. The model is parameterized against experimental data and validated against a range of experimental leach tests under different thermal conditions. A three-dimensional ‘virtual’ heap, under fluctuating meteorological conditions, is simulated. Continuous and intermittent irrigation is investigated, showing copper recovery per unit volume of applied leach solution to be slightly increased for pulse irrigation.
Local TEC Modelling and Forecasting using Neural Networks
Tebabal, A.; Radicella, S. M.; Nigussie, M.; Damtie, B.; Nava, B.; Yizengaw, E.
2017-12-01
Abstract Modelling the Earth's ionospheric characteristics is the focal task for the ionospheric community to mitigate its effect on the radio communication, satellite navigation and technologies. However, several aspects of modelling are still challenging, for example, the storm time characteristics. This paper presents modelling efforts of TEC taking into account solar and geomagnetic activity, time of the day and day of the year using neural networks (NNs) modelling technique. The NNs have been designed with GPS-TEC measured data from low and mid-latitude GPS stations. The training was conducted using the data obtained for the period from 2011 to 2014. The model prediction accuracy was evaluated using data of year 2015. The model results show that diurnal and seasonal trend of the GPS-TEC is well reproduced by the model for the two stations. The seasonal characteristics of GPS-TEC is compared with NN and NeQuick 2 models prediction when the latter one is driven by the monthly average value of solar flux. It is found that NN model performs better than the corresponding NeQuick 2 model for low latitude region. For the mid-latitude both NN and NeQuick 2 models reproduce the average characteristics of TEC variability quite successfully. An attempt of one day ahead forecast of TEC at the two locations has been made by introducing as driver previous day solar flux and geomagnetic index values. The results show that a reasonable day ahead forecast of local TEC can be achieved.
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
Forecasting municipal solid waste generation using artificial intelligence modelling approaches.
Abbasi, Maryam; El Hanandeh, Ali
2016-10-01
Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Wilma E. Waterlander
2016-07-01
Full Text Available Abstract Background 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. Methods/Design 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. Discussion 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
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
International Nuclear Information System (INIS)
Kovalenko, V.I.; Khil'ko, O.S.; Kundas, S.P.
2009-01-01
The work is indicated to the use of artificial neural network (ANN) models in program complex SPS for radioactive substances' migration forecasting in soil. For the problem solution two ANN models are used. One of them forecasts radioactive substances' migration, another carries out forecasting of physical and chemical soil properties. Program complex SPS allows to achieve a low error of forecasting (no more than 5 %) and high training speed. (authors)
Deterministic and heuristic models of forecasting spare parts demand
Directory of Open Access Journals (Sweden)
Ivan S. Milojević
2012-04-01
Full Text Available Knowing the demand of spare parts is the basis for successful spare parts inventory management. Inventory management has two aspects. The first one is operational management: acting according to certain models and making decisions in specific situations which could not have been foreseen or have not been encompassed by models. The second aspect is optimization of the model parameters by means of inventory management. Supply items demand (asset demand is the expression of customers' needs in units in the desired time and it is one of the most important parameters in the inventory management. The basic task of the supply system is demand fulfillment. In practice, demand is expressed through requisition or request. Given the conditions in which inventory management is considered, demand can be: - deterministic or stochastic, - stationary or nonstationary, - continuous or discrete, - satisfied or unsatisfied. The application of the maintenance concept is determined by the technological level of development of the assets being maintained. For example, it is hard to imagine that the concept of self-maintenance can be applied to assets developed and put into use 50 or 60 years ago. Even less complex concepts cannot be applied to those vehicles that only have indicators of engine temperature - those that react only when the engine is overheated. This means that the maintenance concepts that can be applied are the traditional preventive maintenance and the corrective maintenance. In order to be applied in a real system, modeling and simulation methods require a completely regulated system and that is not the case with this spare parts supply system. Therefore, this method, which also enables the model development, cannot be applied. Deterministic models of forecasting are almost exclusively related to the concept of preventive maintenance. Maintenance procedures are planned in advance, in accordance with exploitation and time resources. Since the timing
Optimizing Computing Platforms for Climate-Driven Ecological Forecasting Models
Farley, S. S.; Williams, J. W.
2016-12-01
Species distribution models are widely used, climate-driven ecological forecasting tools that use machine-learning techniques to predict species range shifts and ecological responses to 21st century climate change. As high-resolution modern and fossil biodiversity data becomes increasingly available and statistical learning methods become more computationally intensive, choosing the correct computing configuration on which to run these models becomes more important. With a variety of low-cost cloud and desktop computing options available, users of forecasting models must balance performance gains achieved by provisioning more powerful hardware with the cost of using these resources. We present a framework for estimating the optimal computing solution for a given modeling activity. We argue that this framework is capable of identifying the optimal computing solution - the one that maximizes model accuracy while minimizing resource cost and computing time. Our framework is built on constituent models of algorithm execution time, predictive skill, and computing cost. We demonstrate the results of the framework using four leading species distribution models: multivariate adaptive regression splines, generalized additive models, support vector machines, and boosted regression trees. The constituent models themselves are shown to have high predictive accuracy, and can be used independently to estimate the effects of using larger input datasets, such as those that incorporate data from the fossil record. When used together, our framework shows highly significant predictive ability, and is designed to be used by researchers to inform future computing provisioning strategies.
Forecasting Demand Using Survival Modeling: an application to US prisons
Directory of Open Access Journals (Sweden)
Joanna Baker
1994-11-01
Full Text Available A systems approach to modeling demand which incorporates survival modeling is applied to the problem of prison population projection. The approach models the flow of inmates through the prison system and differs from earlier approaches by exploiting the differences in the incarceration hazard rates of individuals in the general population and those who have previously been incarcerated and explicitly considering the impact of constrained prison capacity on release policy and future admissions. The methodology capitalizes on the impact of recursion in the prison population and reduces the amount and complexity of data required for long-term forecasts.. First-time arrivals to prison are modeled as a Poisson process arising from the general population; recidivist arrivals are modeled using a failure model, where the reincarceration hazard rate is a function of age and race. The model is demonstrated for the state of North Carolina located in the Southeastern region of the United States. The effect of limited prison capacity on the mean of the time-served distribution is shown. The results demonstrate that an early release policy will generate an increase in prison admissions through the return to prison of former inmates. Further, the results show that a systems approach to modeling of prison demand which includes the non-linear effect of recidivism, i.e., survival modeling, has a significant impact on the accuracy of forecasts.
Forecasting Austrian national elections: The Grand Coalition model
Aichholzer, Julian; Willmann, Johanna
2014-01-01
Forecasting the outcomes of national elections has become established practice in several democracies. In the present paper, we develop an economic voting model for forecasting the future success of the Austrian ‘grand coalition’, i.e., the joint electoral success of the two mainstream parties SPOE and OEVP, at the 2013 Austrian Parliamentary Elections. Our main argument is that the success of both parties is strongly tied to the accomplishments of the Austrian system of corporatism, that is, the Social Partnership (Sozialpartnerschaft), in providing economic prosperity. Using data from Austrian national elections between 1953 and 2008 (n=18), we rely on the following predictors in our forecasting model: (1) unemployment rates, (2) previous incumbency of the two parties, and (3) dealignment over time. We conclude that, in general, the two mainstream parties benefit considerably from low unemployment rates, and are weakened whenever they have previously formed a coalition government. Further, we show that they have gradually been losing a good share of their voter basis over recent decades. PMID:26339109
HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.
Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee
2017-08-01
Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.
Sol-Terra - AN Operational Space Weather Forecasting Model Framework
Bisi, M. M.; Lawrence, G.; Pidgeon, A.; Reid, S.; Hapgood, M. A.; Bogdanova, Y.; Byrne, J.; Marsh, M. S.; Jackson, D.; Gibbs, M.
2015-12-01
The SOL-TERRA project is a collaboration between RHEA Tech, the Met Office, and RAL Space funded by the UK Space Agency. The goal of the SOL-TERRA project is to produce a Roadmap for a future coupled Sun-to-Earth operational space weather forecasting system covering domains from the Sun down to the magnetosphere-ionosphere-thermosphere and neutral atmosphere. The first stage of SOL-TERRA is underway and involves reviewing current models that could potentially contribute to such a system. Within a given domain, the various space weather models will be assessed how they could contribute to such a coupled system. This will be done both by reviewing peer reviewed papers, and via direct input from the model developers to provide further insight. Once the models have been reviewed then the optimal set of models for use in support of forecast-based SWE modelling will be selected, and a Roadmap for the implementation of an operational forecast-based SWE modelling framework will be prepared. The Roadmap will address the current modelling capability, knowledge gaps and further work required, and also the implementation and maintenance of the overall architecture and environment that the models will operate within. The SOL-TERRA project will engage with external stakeholders in order to ensure independently that the project remains on track to meet its original objectives. A group of key external stakeholders have been invited to provide their domain-specific expertise in reviewing the SOL-TERRA project at critical stages of Roadmap preparation; namely at the Mid-Term Review, and prior to submission of the Final Report. This stakeholder input will ensure that the SOL-TERRA Roadmap will be enhanced directly through the input of modellers and end-users. The overall goal of the SOL-TERRA project is to develop a Roadmap for an operational forecast-based SWE modelling framework with can be implemented within a larger subsequent activity. The SOL-TERRA project is supported within
The Use of Some Forecasting Methods and SWOT Analysis in the Selected Processes of Foundry
Directory of Open Access Journals (Sweden)
Szymszal J.
2017-12-01
Full Text Available Forecasting and analysis SWOT are helping tools in the business activity, because under conditions of dynamic changes in both closer and more distant surroundings, reliable, forward-looking information and trends analysis are playing a decisive role. At present, the ability to use available data in forecasting and other analyzes according with changes in business environment are the key managerial skills required, since both forecasting and SWOT analysis are a integral part of the management process, and the appropriate level of forecasting knowledge is increasingly appreciated. Examples of practical use of some forecasting methods in optimization of the procurement, production and distribution processes in foundries are given. The possibilities of using conventional quantitative forecasting methods based on econometric and adaptive models applying the creep trend and harmonic weights are presented. The econometric models were additionally supplemented with the presentation of error estimation methodology, quality assessment and statistical verification of the forecast. The possibility of using qualitative forecasts based on SWOT analysis was also mentioned.
Econometric Analysis of Financial Derivatives: An Overview
Chang, Chia-Lin; McAleer, Michael
2014-01-01
One of the fastest growing areas in empirical finance, and also one of the least rigorously analyzed, especially from a financial econometrics perspective, is the econometric analysis of financial derivatives, which are typically complicated and difficult to analyze. The purpose of this special issue of the journal on “Econometric Analysis of Financial Derivatives” is to highlight several areas of research by leading academics in which novel econometric, financial econometric, mathematical fi...
Forecast model of landslides in a short time
International Nuclear Information System (INIS)
Sanchez Lopez, Reinaldo
2006-01-01
The IDEAM in development of their functions as member of the national technical committee for the prevention and disasters attention (SNPAD) accomplishes the follow-up, monitoring and forecast in real time of the environmental dynamics that in extreme situations constitute threats and natural risks. One of the frequent dynamics and of greater impact is related to landslides, those that affect persistently the life of the persons, the infrastructure, the socioeconomic activities and the balance of the environment. The landslide in Colombia and in the world are caused mainly by effects of the rain, due to that, IDEAM has come developing forecast model, as an instrument for risk management in a short time. This article presents aspects related to their structure, operation, temporary space resolution, products, results, achievements and projections of the model. Conceptually, the model is support by the principle of the dynamic temporary - space, of the processes that consolidate natural hazards, particularly in areas where the man has come building the risk. Structurally, the model is composed by two sub-models; the general susceptibility of the earthly model and the critical rain model as a denotative factor, that consolidate the hazard process. In real time, the model, works as a GIS, permitting the automatic zoning of the landslides hazard for issue public advisory warming to help makers decisions on the risk that cause frequently these events, in the country
Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh
2014-01-01
Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.
Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model
Ibrahim, R. I.; Ngataman, N.; Abrisam, W. N. A. Wan Mohd
2017-09-01
Improvement in life expectancies has driven further declines in mortality. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all forecasting methods, the Lee-Carter model has been widely accepted by the actuarial community and Heligman-Pollard model has been widely used by researchers in modelling and forecasting future mortality. Therefore, this paper only focuses on Lee-Carter model and Heligman-Pollard model. The main objective of this paper is to investigate how accurately these two models will perform using Malaysian data. Since these models involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 8.0 (MATLAB 8.0) software will be used to estimate the parameters of the models. Autoregressive Integrated Moving Average (ARIMA) procedure is applied to acquire the forecasted parameters for both models as the forecasted mortality rates are obtained by using all the values of forecasted parameters. To investigate the accuracy of the estimation, the forecasted results will be compared against actual data of mortality rates. The results indicate that both models provide better results for male population. However, for the elderly female population, Heligman-Pollard model seems to underestimate to the mortality rates while Lee-Carter model seems to overestimate to the mortality rates.
Molenaars, Tomas K.; Reinerink, Nick H.; Hemminga, Marcus A.
2013-01-01
We define a parameter representing the relative forecast performance to compare forecasting results of different methods. By using this parameter, we analyze the performance of the dynamic Nelson-Siegel model and, for comparison, the first order autoregressive (AR(1)) model applied to a set of US bond yield data that covers a time span from November 1971 to December 2008. As a reference, we take the random walk model applied to the yield data. Our findings indicate that none of the models can...
A Feature Fusion Based Forecasting Model for Financial Time Series
Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie
2014-01-01
Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455
Application of artificial intelligence models in water quality forecasting.
Yeon, I S; Kim, J H; Jun, K W
2008-06-01
The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error.
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Day-ahead wind speed forecasting using f-ARIMA models
International Nuclear Information System (INIS)
Kavasseri, Rajesh G.; Seetharaman, Krithika
2009-01-01
With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24 h) and two-day-ahead (48 h) horizons. The models are applied to wind speed records obtained from four potential wind generation sites in North Dakota. The forecasted wind speeds are used in conjunction with the power curve of an operational (NEG MICON, 750 kW) turbine to obtain corresponding forecasts of wind power production. The forecast errors in wind speed/power are analyzed and compared with the persistence model. Results indicate that significant improvements in forecasting accuracy are obtained with the proposed models compared to the persistence method. (author)
Forecasting US renewables in the national energy modelling system
International Nuclear Information System (INIS)
Diedrich, R.; Petersik, T.W.
2001-01-01
The Energy information Administration (EIA) of the US Department of Energy (DOE) forecasts US renewable energy supply and demand in the context of overall energy markets using the National Energy Modelling System (NEMS). Renewables compete with other supply and demand options within the residential, commercial, industrial, transportation, and electricity sectors of the US economy. NEMS forecasts renewable energy for grid-connected electricity production within the Electricity Market Module (EM), and characterizes central station biomass, geothermal, conventional hydroelectric, municipal solid waste, solar thermal, solar photovoltaic, and wind-powered electricity generating technologies. EIA's Annual Energy Outlook 1998, projecting US energy markets, forecasts marketed renewables to remain a minor part of US energy production and consumption through to 2020. The USA is expected to remain primarily a fossil energy producer and consumer throughout the period. An alternative case indicates that biomass, wind, and to some extent geothermal power would likely increase most rapidly if the US were to require greater use of renewables for power supply, though electricity prices would increase somewhat. (author)
Perturbations of modeling and forecast of karachi coastal region seawater
International Nuclear Information System (INIS)
Hussain, M.A.; Abbas, S.; Ansari, M.R.K.; Zaffar, A.
2013-01-01
Global warming is now a stark reality affecting the humanity in many hazardous ways. Continuous floods in Pakistan in past two years are an eye opener in this regard. A great loss of property, agriculture and life as a result of these floods suggests for an intelligent monitoring of the future projections of climate change and global warming. This is necessary because the harmful impacts of natural hazards can be coped and alleviated with a good planning in advance. This monitoring demands for enhanced forecasting capabilities, use of better analytical techniques and a clear determination and study of the controlling factors. Karachi is a coastal city which is also the industrial hub of Pakistan. Moreover, it is among one of the largest metropolitans of the world. So expectedly is most suitable for the study of high level of complex natural and anthropogenic activities. It is peculiar in the sense that it has two summer seasons, a situation scarcely observable on the globe. Here, summer season seawater temperature fluctuations are studied with the help of Seasonal Autoregressive Integrated Moving Average (SARIMA) models and short- and long-term forecasts are made. Our short-term forecasts determine months for the summer wise temperature extremes. It appears that the months of May, June, July and August are the months of extreme temperature for the first summer and October is the month of extreme temperature for the second summer. The long-term forecasts predict that 2014, 2016, 2018, and 2019 will be the years of warm summers. The analysis appearing here would be useful for coastal-urban planners in emphasizing the impact of seawater extreme temperatures on urban industrial activities, etc. (author)
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...
Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data.
Liu, Zitao; Hauskrecht, Milos
2016-02-01
Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy.
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.
Forecasting Lightning Threat using Cloud-Resolving Model Simulations
McCaul, Eugene W., Jr.; Goodman, Steven J.; LaCasse, Katherine M.; Cecil, Daniel J.
2008-01-01
Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single
The Econometric Procedures of Specific Transaction Identification
Directory of Open Access Journals (Sweden)
Doszyń Mariusz
2017-06-01
Full Text Available The paper presents the econometric procedures of identifying specific transactions, in which atypical conditions or attributes may occur. These procedures are based on studentized and predictive residuals of the accordingly specified econometric models. The dependent variable is a unit transactional price, and explanatory variables are both the real properties’ attributes and accordingly defined artificial binary variables. The utility of the proposed method has been verified by means of a real market data base. The proposed procedures can be helpful during the property valuation process, making it possible to reject real properties that are specific (both from the point of view of the transaction conditions and the properties’ attributes and, consequently, to select an appropriate set of similar attributes that are essential for the valuation process.
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 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...
Forecasting energy consumption using a grey model improved by incorporating genetic programming
International Nuclear Information System (INIS)
Lee, Yi-Shian; Tong, Lee-Ing
2011-01-01
Energy consumption is an important economic index, which reflects the industrial development of a city or a country. Forecasting energy consumption by conventional statistical methods usually requires the making of assumptions such as the normal distribution of energy consumption data or on a large sample size. However, the data collected on energy consumption are often very few or non-normal. Since a grey forecasting model, based on grey theory, can be constructed for at least four data points or ambiguity data, it can be adopted to forecast energy consumption. In some cases, however, a grey forecasting model may yield large forecasting errors. To minimize such errors, this study develops an improved grey forecasting model, which combines residual modification with genetic programming sign estimation. Finally, a real case of Chinese energy consumption is considered to demonstrate the effectiveness of the proposed forecasting model.
Spatio-temporal modelling for short term wind power forecasts. Why, when and how.
Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre
2017-04-01
This study is based on a case study of 349 wind farms in Western Denmark with available energy production every 15 minutes for 6 years. Our aim is to do short term forecasting up to 5 hours ahead based on previous observations. We want sharp and calibrated probabilistic forecasts for both individual wind farms and for aggregated energy production, for example the energy production in the whole region. To obtain this we propose two Bayesian spatio-temporal models, and obtain full probabilistic forecasts of wind power. The models are based on the stochastic partial differential equation (SPDE) approach to spatial-temporal modelling which enables fast inference using integrated nested Laplace approximations (INLA) as well as dimension reduction. We provide detailed analysis on the forecast performances on the individual and aggregated level based on appropriate metrics tailored for probability forecasts for both the spatial temporal models as well as for temporal models for individual wind farms. The case study as well as simulation studies demonstrate that forecasts that are individually reliable do not need to produce an aggregated forecasts that are reliable. Indeed, the case study shows that even when all individual forecasts are calibrated can the aggregated forecasts be so uncalibrated that less that 20% of the observations fall within the 95% forecast interval. T he results and methodology are both relevant for wind power forecasts in other regions as well as for spatial-temporal modeling and decisions in general.
Directory of Open Access Journals (Sweden)
Suhartono Suhartono
2005-01-01
Full Text Available Many business and economic time series are non-stationary time series that contain trend and seasonal variations. Seasonality is a periodic and recurrent pattern caused by factors such as weather, holidays, or repeating promotions. A stochastic trend is often accompanied with the seasonal variations and can have a significant impact on various forecasting methods. In this paper, we will investigate and compare some forecasting methods for modeling time series with both trend and seasonal patterns. These methods are Winter's, Decomposition, Time Series Regression, ARIMA and Neural Networks models. In this empirical research, we study on the effectiveness of the forecasting performance, particularly to answer whether a complex method always give a better forecast than a simpler method. We use a real data, that is airline passenger data. The result shows that the more complex model does not always yield a better result than a simpler one. Additionally, we also find the possibility to do further research especially the use of hybrid model by combining some forecasting method to get better forecast, for example combination between decomposition (as data preprocessing and neural network model.
Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models
Directory of Open Access Journals (Sweden)
Zhang Chi
2016-01-01
Full Text Available Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.
Lightning Forecasts and Data Assimilation into Numerical Weather Prediction Models
MacGorman, D. R.; Mansell, E. R.; Fierro, A.; Ziegler, C.
2012-12-01
This presentation reviews two aspects of lightning in numerical weather prediction (NWP) models: forecasting lightning and assimilating lightning data into NWP models to improve weather forecasts. One of the earliest routine forecasts of lightning was developed for fire weather operations. This approach used a multi-parameter regression analysis of archived cloud-to-ground (CG) lightning data and archived NWP data to optimize the combination of model state variables to use in forecast equations for various CG rates. Since then, understanding of how storms produce lightning has improved greatly. As the treatment of ice in microphysics packages used by NWP models has improved and the horizontal resolution of models has begun approaching convection-permitting scales (with convection-resolving scales on the horizon), it is becoming possible to use this improved understanding in NWP models to predict lightning more directly. An important role for data assimilation in NWP models is to depict the location, timing, and spatial extent of thunderstorms during model spin-up so that the effects of prior convection that can strongly influence future thunderstorm activity, such as updrafts and outflow boundaries, can be included in the initial state of a NWP model run. Radar data have traditionally been used, but systems that map lightning activity with varying degrees of coverage, detail, and detection efficiency are now available routinely over large regions and reveal information about storms that is complementary to the information provided by radar. Because data from lightning mapping systems are compact, easily handled, and reliably indicate the location and timing of thunderstorms, even in regions with little or no radar coverage, several groups have investigated techniques for assimilating these data into NWP models. This application will become even more valuable with the launch of the Geostationary Lightning Mapper on the GOES-R satellite, which will extend routine
International Nuclear Information System (INIS)
Pollitt, Hector; Park, Seung-Joon; Lee, Soocheol; Ueta, Kazuhiro
2014-01-01
In this paper we consider future options for Japanese energy and climate policy. We assess the economic and environmental impacts of changing the share of electricity generated by nuclear power and varying the mid-term GHG targets. The quantitative approach we use is based on the global macro-econometric E3MG model. Our analysis reveals that the cost of denuclearisation to Japanese GDP is close to zero, and for employment the impact is slightly positive. Our results also show a double-dividend effect if (revenue-neutral) carbon taxes are levied in order to meet the GHG reduction targets, and this double-dividend effect is largest in the scenarios without nuclear power. However, our analysis suggests that a very high carbon tax rate would have to be imposed in order to achieve a 25% reduction in GHG emissions in 2020 (compared to 1990 levels) while simultaneously phasing out nuclear power. - Highlights: • We modelled 12 scenarios for Japan with different shares for nuclear power and different emission targets. • The results showed that phasing out nuclear power would have at most a very small reduction in GDP. • If a carbon tax with revenue recycling is applied, there could be an increase in GDP. • But the carbon price required to meet Japan's 25% emission reduction target is very high if the share of nuclear power is reduced
Space Weather Forecasts Driven by the ADAPT Model
Henney, C. J.; Arge, C. N.; Shurkin, K.; Schooley, A. K.; Hock, R. A.; White, S.
2015-12-01
In this presentation, we highlight recent progress to forecast key space weather parameters with the ADAPT (Air Force Data Assimilative Photospheric flux Transport) model. Driven by a magnetic flux transport model, ADAPT evolves global solar magnetic maps forward 1 to 7 days in the future to provide realistic estimates of the solar near-side field distribution used to forecast the solar wind, F10.7 (i.e., the solar 10.7 cm radio flux), extreme ultraviolet (EUV) and far ultraviolet (FUV) irradiance. Input to the ADAPT model includes solar near-side estimates of the inferred photospheric magnetic field from space-based (i.e., HMI) and ground-based (e.g., GONG & VSM) instruments. We summarize the recent findings that: 1) the sum of the absolute value of strong magnetic fields, associated with sunspots, is shown to correlate well with the observed daily F10.7 variability (Henney et al. 2012); and 2) the sum of the absolute value of weak magnetic fields, associated with plage regions, is shown to correlate well with EUV and FUV irradiance variability (Henney et al. 2015). In addition, recent progress to utilize the ADAPT global maps as input to the Wang-Sheeley-Arge (WSA) coronal and solar wind model is presented. We also discuss the challenges of observing less than half of the solar surface at any given time and the need for future magnetograph instruments near L1 and L5.
Building a House Prices Forecasting Model in Hong Kong
Directory of Open Access Journals (Sweden)
Xin Janet
2012-11-01
Full Text Available This paper builds a house prices forecasting model for private residential houses in HongKong, based on general macroeconomic indicators, housing related data and demographicfactors for the period of 1980 to 2001. A reduce form economic model has been derivedfrom a multiple regression analysis where three sets and eight models were derived foranalysis and comparison. It is found that household income, land supply, population andmovements in the Hang Seng Index play an important role in explaining house pricemovements in Hong Kong. In addition, political events, as identified, cannot be ignored.However, the results of the models are unstable. It is suggested that the OLS may nota best method for house prices model in Hong Kong situation. Alternative methods aresuggested.
Forecast model applied to quality control with autocorrelational data
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Adriano Mendonça Souza
2013-11-01
Full Text Available This research approaches the prediction models applied to industrial processes, in order to check the stability of the process by means of control charts, applied to residues from linear modeling. The data used for analysis refers to the moisture content, permeability and compression resistance to the green (RCV, belonging to the casting process of green sand molding in A Company, which operates in the casting and machining, for which dynamic multivariate regression model was set. As the observations were auto-correlated, it was necessary to seek a mathematical model that produces independent and identically distribuibed residues. The models found make possible to understand the variables behavior, assisting in the achievement of the forecasts and in the monitoring of the referred process. Thus, it can be stated that the moisture content is very unstable comparing to the others variables.
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
DEFF Research Database (Denmark)
Draxl, Caroline; Hahmann, Andrea N.; Pena Diaz, Alfredo
2014-01-01
with different PBL parameterizations at one coastal site over western Denmark. The evaluation focuses on determining which PBL parameterization performs best for wind energy forecasting, and presenting a validation methodology that takes into account wind speed at different heights. Winds speeds at heights...... regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by theWeather Research and Forecasting model using seven sets of simulations...
Econometrics in R: Past, Present, and Future
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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.
COP21 climate negotiators' responses to climate model forecasts
Bosetti, Valentina; Weber, Elke; Berger, Loïc; Budescu, David V.; Liu, Ning; Tavoni, Massimo
2017-02-01
Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most effectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models' forecasts. We randomized the way information was provided across participants using three different formats similar to those used in Intergovernmental Panel on Climate Change reports. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more effective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models' forecasts than policymakers. There was also no effect of presentation format in the MBA sample. These results highlight the importance of testing visualization tools directly on the population of interest.
Pharmaceutical expenditure forecast model to support health policy decision making
Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher
2014-01-01
Background and objective With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project ‘European Union (EU) Pharmaceutical expenditure forecast’ – http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods A model was built to assess policy scenarios’ impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Results Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Conclusions Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate
International Nuclear Information System (INIS)
Estomin, S.L.; Beach, J.E.; Goldsmith, J.V.
1991-05-01
The two-volume report presents the results of an econometric forecast of peak load and electric power demand for the Baltimore Gas and Electric Company (BG ampersand E) through the year 2009. Separate energy sales models were estimated for residential sales in Baltimore City, residential sales in the BG ampersand E service area excluding Baltimore City, commercial sales, industrial sales, streetlighting sales, and Company use plus losses. Econometric equations were also estimated for electric space heating and air conditioning saturation in Baltimore City and in the remainder of the BG ampersand E service territory. In addition to the energy sales models and the electric space conditioning saturation models, econometric models of summer and winter peak demand on the BG ampersand E system were estimated
Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility
Tuba, Zoltán; Bottyán, Zsolt
2018-04-01
Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.
Forecasting the Reference Evapotranspiration Using Time Series Model
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H. Zare Abyaneh
2016-10-01
Full Text Available Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations. Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1, the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data. Table 1. The geographical location and climate conditions of the synoptic stations Station\tGeographical location\tAltitude (m\tMean air temperature (°C\tMean precipitation (mm\tClimate, according to the De Martonne index classification Longitude (E\tLatitude (N Annual\tMin. and Max. Esfahan\t51° 40'\t32° 37'\t1550.4\t16.36\t9.4-23.3\t122\tArid Semnan\t53° 33'\t35° 35'\t1130.8\t18.0\t12.4-23.8\t140\tArid Shiraz\t52° 36'\t29° 32'\t1484\t18.0\t10.2-25.9\t324\tSemi-arid Kerman\t56° 58'\t30° 15'\t1753.8\t15.6\t6.7-24.6\t142\tArid Yazd\t54° 17'\t31° 54'\t1237.2\t19.2\t11.8-26.0\t61\tArid Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference
Lage, M J; Barber, B L; Bala, M; McCollam, P L; Ball, D E
2000-12-01
To examine the effect of abciximab treatment on intensive care length of stay for patients undergoing percutaneous coronary intervention (PCI). A retrospective study conducted in a naturalistic setting. A 2-stage econometric model was used to control for the influence of possible selection bias across categories of patients and for both observable and unobservable factors correlated with each patient's treatment selection and length of stay in intensive care. Multivariate analysis was applied to control for a wide range of factors (patient demographics, insurance provider, health conditions, admission and discharge information, and hospital characteristics) that may influence intensive care length of stay. Retrospective data were obtained from HCIA's Clinical Pathways Database. Patients (n = 13,364) who were hospitalised in any of 87 hospitals across the US over the period from October 1, 1995 to December 1, 1996. After controlling for high-risk indications and selection bias, results indicated that administration of abciximab was associated with a significantly shorter length of stay in intensive care compared with not administering a GPIIb/IIIa inhibitor (0.45 fewer days; p < or = 0.0001). In a subgroup analysis of patients having an acute myocardial infarction (n = 4793), administration of abciximab was also associated with a significantly shorter intensive care stay (0.27 fewer days; p < 0.0001). Results of this study indicate that the administration of abciximab is associated with a reduction in the length of stay in intensive care. This reduction implies potential cost offsets for patients undergoing PCI who receive abciximab.
Dynamic Hybrid Model for Short-Term Electricity Price Forecasting
Marin Cerjan; Marin Matijaš; Marko Delimar
2014-01-01
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 det...
Stuparu, Dana; Bachmann, Daniel; Bogaard, Tom; Twigt, Daniel; Verkade, Jan; de Bruijn, Karin; de Leeuw, Annemargreet
2017-04-01
Flood forecasts, warning and emergency response are important components in flood risk management. Most flood forecasting systems use models to translate weather predictions to forecasted discharges or water levels. However, this information is often not sufficient for real time decisions. A sound understanding of the reliability of embankments and flood dynamics is needed to react timely and reduce the negative effects of the flood. Where are the weak points in the dike system? When, how much and where the water will flow? When and where is the greatest impact expected? Model-based flood impact forecasting tries to answer these questions by adding new dimensions to the existing forecasting systems by providing forecasted information about: (a) the dike strength during the event (reliability), (b) the flood extent in case of an overflow or a dike failure (flood spread) and (c) the assets at risk (impacts). This work presents three study-cases in which such a set-up is applied. Special features are highlighted. Forecasting of dike strength. The first study-case focusses on the forecast of dike strength in the Netherlands for the river Rhine branches Waal, Nederrijn and IJssel. A so-called reliability transformation is used to translate the predicted water levels at selected dike sections into failure probabilities during a flood event. The reliability of a dike section is defined by fragility curves - a summary of the dike strength conditional to the water level. The reliability information enhances the emergency management and inspections of embankments. Ensemble forecasting. The second study-case shows the setup of a flood impact forecasting system in Dumfries, Scotland. The existing forecasting system is extended with a 2D flood spreading model in combination with the Delft-FIAT impact model. Ensemble forecasts are used to make use of the uncertainty in the precipitation forecasts, which is useful to quantify the certainty of a forecasted flood event. From global
Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
Khan, Muhammad Yousaf; Mittnik, Stefan
2017-11-01
In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.
Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
Khan, Muhammad Yousaf; Mittnik, Stefan
2018-01-01
In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.
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Gayo Willy
2016-01-01
Full Text Available Philippine Stock Exchange Composite Index (PSEi is the main stock index of the Philippine Stock Exchange (PSE. PSEi is computed using a weighted mean of the top 30 publicly traded companies in the Philippines, called component stocks. It provides a single value by which the performance of the Philippine stock market is measured. Unfortunately, these weights, which may vary for every trading day, are not disclosed by the PSE. In this paper, we propose a model of forecasting the PSEi by estimating the weights based on historical data and forecasting each component stock using Monte Carlo simulation based on a Geometric Brownian Motion (GBM assumption. The model performance is evaluated and its forecast compared is with the results using a direct GBM forecast of PSEi over different forecast periods. Results showed that the forecasts using WGBM will yield smaller error compared to direct GBM forecast of PSEi.
Frontiers in Time Series and Financial Econometrics : An overview
S. Ling (Shiqing); M.J. McAleer (Michael); H. Tong (Howell)
2015-01-01
markdownabstract__Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time
Frontiers in Time Series and Financial Econometrics: An Overview
S. Ling (Shiqing); M.J. McAleer (Michael); H. Tong (Howell)
2015-01-01
markdownabstract__Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time
Model-free aftershock forecasts constructed from similar sequences in the past
van der Elst, N.; Page, M. T.
2017-12-01
The basic premise behind aftershock forecasting is that sequences in the future will be similar to those in the past. Forecast models typically use empirically tuned parametric distributions to approximate past sequences, and project those distributions into the future to make a forecast. While parametric models do a good job of describing average outcomes, they are not explicitly designed to capture the full range of variability between sequences, and can suffer from over-tuning of the parameters. In particular, parametric forecasts may produce a high rate of "surprises" - sequences that land outside the forecast range. Here we present a non-parametric forecast method that cuts out the parametric "middleman" between training data and forecast. The method is based on finding past sequences that are similar to the target sequence, and evaluating their outcomes. We quantify similarity as the Poisson probability that the observed event count in a past sequence reflects the same underlying intensity as the observed event count in the target sequence. Event counts are defined in terms of differential magnitude relative to the mainshock. The forecast is then constructed from the distribution of past sequences outcomes, weighted by their similarity. We compare the similarity forecast with the Reasenberg and Jones (RJ95) method, for a set of 2807 global aftershock sequences of M≥6 mainshocks. We implement a sequence-specific RJ95 forecast using a global average prior and Bayesian updating, but do not propagate epistemic uncertainty. The RJ95 forecast is somewhat more precise than the similarity forecast: 90% of observed sequences fall within a factor of two of the median RJ95 forecast value, whereas the fraction is 85% for the similarity forecast. However, the surprise rate is much higher for the RJ95 forecast; 10% of observed sequences fall in the upper 2.5% of the (Poissonian) forecast range. The surprise rate is less than 3% for the similarity forecast. The similarity
Multiobjective Optimization for the Forecasting Models on the Base of the Strictly Binary Trees
Nadezhda Astakhova; Liliya Demidova; Evgeny Nikulchev
2016-01-01
The optimization problem dealing with the development of the forecasting models on the base of strictly binary trees has been considered. The aim of paper is the comparative analysis of two optimization variants which are applied for the development of the forecasting models. Herewith the first optimization variant assumes the application of one quality indicator of the forecasting model named as the affinity indicator and the second variant realizes the application of two quality indicators ...
A Capacity Forecast Model for Volatile Data in Maintenance Logistics
Berkholz, Daniel
2009-05-01
Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintaining valuable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network. The article outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning model. This enables a quick response to maintenance orders by employing appropriate measures. The information gained through the model is then systematically applied to forecast both personnel capacities and the demand for spare parts. The improved planning reliability can support MRO service providers in shortening delivery times and reducing stock levels in order to enhance the performance of their maintenance logistics.
A high resolution WRF model for wind energy forecasting
Vincent, Claire Louise; Liu, Yubao
2010-05-01
The increasing penetration of wind energy into national electricity markets has increased the demand for accurate surface layer wind forecasts. There has recently been a focus on forecasting the wind at wind farm sites using both statistical models and numerical weather prediction (NWP) models. Recent advances in computing capacity and non-hydrostatic NWP models means that it is possible to nest mesoscale models down to Large Eddy Simulation (LES) scales over the spatial area of a typical wind farm. For example, the WRF model (Skamarock 2008) has been run at a resolution of 123 m over a wind farm site in complex terrain in Colorado (Liu et al. 2009). Although these modelling attempts indicate a great hope for applying such models for detailed wind forecasts over wind farms, one of the obvious challenges of running the model at this resolution is that while some boundary layer structures are expected to be modelled explicitly, boundary layer eddies into the inertial sub-range can only be partly captured. Therefore, the amount and nature of sub-grid-scale mixing that is required is uncertain. Analysis of Liu et al. (2009) modelling results in comparison to wind farm observations indicates that unrealistic wind speed fluctuations with a period of around 1 hour occasionally occurred during the two day modelling period. The problem was addressed by re-running the same modelling system with a) a modified diffusion constant and b) two-way nesting between the high resolution model and its parent domain. The model, which was run with horizontal grid spacing of 370 m, had dimensions of 505 grid points in the east-west direction and 490 points in the north-south direction. It received boundary conditions from a mesoscale model of resolution 1111 m. Both models had 37 levels in the vertical. The mesoscale model was run with a non-local-mixing planetary boundary layer scheme, while the 370 m model was run with no planetary boundary layer scheme. It was found that increasing the
Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa
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Tadesse Kassahun Birhanu
2017-12-01
Full Text Available Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI and Hannan–Quinn (HQ criteria, SARIMA (3, 0, 2 x (3, 1, 312 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.
Forecast Jointed Rock Mass Compressive Strength Using a Numerical Model
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Protosenya Anatoliy
2016-01-01
Full Text Available The method of forecasting the strength of the jointed rock mass by numerical modeling of finite element method in ABAQUS was described. The paper presents advantages of this method to solve the problem of determining the mechanical characteristics of jointed rock mass and the basic steps of creating a numerical geomechanical model of jointed rock mass and numerical experiment. Numerical simulation was carried out with jointed rock mass in order to obtain the ratio of strain and stress while loading the numerical model, determining parameters of quantitative assessment of the impact of the discontinuities orientation on the value of the compressive strength, compressive strength anisotropy. The results of the numerical experiment are compared with the data of experimental studies investigations. Innovative materials and structures are analyzed in this paper. The results that were obtained by calculation show qualitative agreement with the results of laboratory experiments of jointed rock mass.
Artificial Neural Network versus Linear Models Forecasting Doha Stock Market
Yousif, Adil; Elfaki, Faiz
2017-12-01
The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.
Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models
Elkantassi, Soumaya
2017-04-01
Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we propose and analyze stochastic wind power forecast models described by parametrized stochastic differential equations, which introduce appropriate fluctuations in numerical forecast outputs. We use an approximate maximum likelihood method to infer the model parameters taking into account the time correlated sets of data. Furthermore, we study the validity and sensitivity of the parameters for each model. We applied our models to Uruguayan wind power production as determined by historical data and corresponding numerical forecasts for the period of March 1 to May 31, 2016.
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V. A. Bell
2000-01-01
Full Text Available A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid within the higher resolution model (5 km grid. A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii advected, steady-state predictions from the dynamic model, using 'inferred updraughts', provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment
Improving the Model for Energy Consumption Load Demand Forecasting
Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak
This paper proposes an application of a filter method in preprocessing stage for mid-term load demand forecasting to improve electricity load forecasting and to guarantee satisfactory forecasting accuracy. Case study employs the historical electricity consumption demand data in Thailand which were recorded in the 12 years of 1997 through to 2007. The load demand forecasted value is used for unit commitment and fuel reserve planning in the power system. This method consists of a trend component and a cyclical component decomposed from the original load demand using the Hodrick-Prescott (HP) filter in the preprocessing stage and the forecasting of each component using Double Neural Networks (DNNs) in the forecasting stage. Experimental results show that with preprocessing before forecasting can predict the load demand better than that without preprocessing.
WRF-Fire: coupled weather-wildland fire modeling with the weather research and forecasting model
Janice L. Coen; Marques Cameron; John Michalakes; Edward G. Patton; Philip J. Riggan; Kara M. Yedinak
2012-01-01
A wildland fire behavior module (WRF-Fire) was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties...
Corzo Perez, G.A.
2009-01-01
This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following
Corzo Perez, G.A.
2009-01-01
This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following
The long-run forecasting of energy prices using the model of shifting trend
International Nuclear Information System (INIS)
Radchenko, Stanislav
2005-01-01
Developing models for accurate long-term energy price forecasting is an important problem because these forecasts should be useful in determining both supply and demand of energy. On the supply side, long-term forecasts determine investment decisions of energy-related companies. On the demand side, investments in physical capital and durable goods depend on price forecasts of a particular energy type. Forecasting long-run rend movements in energy prices is very important on the macroeconomic level for several developing countries because energy prices have large impacts on their real output, the balance of payments, fiscal policy, etc. Pindyck (1999) argues that the dynamics of real energy prices is mean-reverting to trend lines with slopes and levels that are shifting unpredictably over time. The hypothesis of shifting long-term trend lines was statistically tested by Benard et al. (2004). The authors find statistically significant instabilities for coal and natural gas prices. I continue the research of energy prices in the framework of continuously shifting levels and slopes of trend lines started by Pindyck (1999). The examined model offers both parsimonious approach and perspective on the developments in energy markets. Using the model of depletable resource production, Pindyck (1999) argued that the forecast of energy prices in the model is based on the long-run total marginal cost. Because the model of a shifting trend is based on the competitive behavior, one may examine deviations of oil producers from the competitive behavior by studying the difference between actual prices and long-term forecasts. To construct the long-run forecasts (10-year-ahead and 15-year-ahead) of energy prices, I modify the univariate shifting trends model of Pindyck (1999). I relax some assumptions on model parameters, the assumption of white noise error term, and propose a new Bayesian approach utilizing a Gibbs sampling algorithm to estimate the model with autocorrelation. To
A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting
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Weishang Guo
2017-09-01
Full Text Available Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND method, fruit fly optimization algorithm (FOA, and least square support vector machine (LSSVM model, namely BND-FOA-LSSVM model. Firstly, the original electricity price time series were decomposed into deterministic term, periodic term, and stochastic term by using BND model. Then, these three decomposed terms were forecasted by employing LSSVM model, respectively. Meanwhile, to improve the forecasting performance, a new swarm intelligence optimization algorithm FOA was used to automatically determine the optimal parameters of LSSVM model for deterministic term forecasting, periodic term forecasting, and stochastic term forecasting. Finally, the forecasting result of electricity price can be obtained by multiplying the forecasting values of these three terms. The results show the mean absolute percentage error (MAPE, root mean square error (RMSE and mean absolute error (MAE of the proposed BND-FOA-LSSVM model are respectively 3.48%, 11.18 Yuan/MWh and 9.95 Yuan/MWh, which are much smaller than that of LSSVM, BND-LSSVM, FOA-LSSVM, auto-regressive integrated moving average (ARIMA, and empirical mode decomposition (EMD-FOA-LSSVM models. The proposed BND-FOA-LSSVM model is effective and practical for electricity price forecasting, which can improve the electricity price forecasting accuracy.
Data on photovoltaic power forecasting models for Mediterranean climate.
Malvoni, M; De Giorgi, M G; Congedo, P M
2016-06-01
The weather data have a relevant impact on the photovoltaic (PV) power forecast, furthermore the PV power prediction methods need the historical data as input. The data presented in this article concern measured values of ambient temperature, module temperature, solar radiation in a Mediterranean climate. Hourly samples of the PV output power of 960kWP system located in Southern Italy were supplied for more 500 days. The data sets, given in , were used in DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015) [1] to compare Artificial Neural Networks and Least Square Support Vector Machines. It was found that LS-SVM with Wavelet Decomposition (WD) outperforms ANN method. In DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016) [2] the same data were used for comparing different strategies for multi-step ahead forecast based on the hybrid Group Method of Data Handling networks and Least Square Support Vector Machine. The predicted PV power values by three models were reported in .
Briggs, Adam D M; Kehlbacher, Ariane; Tiffin, Richard; Garnett, Tara; Rayner, Mike; Scarborough, Peter
2013-10-22
To model the impact on chronic disease of a tax on UK food and drink that internalises the wider costs to society of greenhouse gas (GHG) emissions and to estimate the potential revenue. An econometric and comparative risk assessment modelling study. The UK. The UK adult population. Two tax scenarios are modelled: (A) a tax of £2.72/tonne carbon dioxide equivalents (tCO2e)/100 g product applied to all food and drink groups with above average GHG emissions. (B) As with scenario (A) but food groups with emissions below average are subsidised to create a tax neutral scenario. Primary outcomes are change in UK population mortality from chronic diseases following the implementation of each taxation strategy, the change in the UK GHG emissions and the predicted revenue. Secondary outcomes are the changes to the micronutrient composition of the UK diet. Scenario (A) results in 7770 (95% credible intervals 7150 to 8390) deaths averted and a reduction in GHG emissions of 18 683 (14 665to 22 889) ktCO2e/year. Estimated annual revenue is £2.02 (£1.98 to £2.06) billion. Scenario (B) results in 2685 (1966 to 3402) extra deaths and a reduction in GHG emissions of 15 228 (11 245to 19 492) ktCO2e/year. Incorporating the societal cost of GHG into the price of foods could save 7770 lives in the UK each year, reduce food-related GHG emissions and generate substantial tax revenue. The revenue neutral scenario (B) demonstrates that sustainability and health goals are not always aligned. Future work should focus on investigating the health impact by population subgroup and on designing fiscal strategies to promote both sustainable and healthy diets.
Regressional modeling and forecasting of economic growth for arkhangelsk region
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Robert Mikhailovich Nizhegorodtsev
2012-12-01
Full Text Available The regression models of GRP, considering the impact of three main factors: investment in fixed assets, wages amount, and, importantly, the innovation factor –the expenditures for research and development, are constructed in this paper on the empirical data for Arkhangelsk region. That approach permits to evaluate explicitly the contribution of innovation to economic growth. Regression analysis is the main research instrument, all calculations areperformedin the Microsoft Excel. There were made meaningful conclusions regarding the potential of the region's GRP growth by various factors, including impacts of positive and negative time lags. Adequate and relevant models are the base for estimation and forecasting values of the dependent variable (GRP and evaluating their confidence intervals. The invented method of research can be used in factor assessment and prediction of regional economic growth, including growth by expectations.
An artificial neural network model for rainfall forecasting in Bangkok, Thailand
Directory of Open Access Journals (Sweden)
N. Q. Hung
2009-08-01
Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.
eWaterCycle: A global operational hydrological forecasting model
van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin
2015-04-01
Development of an operational hyper-resolution hydrological global model is a central goal of the eWaterCycle project (www.ewatercycle.org). This operational model includes ensemble forecasts (14 days) to predict water related stress around the globe. Assimilation of near-real time satellite data is part of the intended product that will be launched at EGU 2015. The challenges come from several directions. First, there are challenges that are mainly computer science oriented but have direct practical hydrological implications. For example, we aim to make use as much as possible of existing standards and open-source software. For example, different parts of our system are coupled through the Basic Model Interface (BMI) developed in the framework of the Community Surface Dynamics Modeling System (CSDMS). The PCR-GLOBWB model, built by Utrecht University, is the basic hydrological model that is the engine of the eWaterCycle project. Re-engineering of parts of the software was needed for it to run efficiently in a High Performance Computing (HPC) environment, and to be able to interface using BMI, and run on multiple compute nodes in parallel. The final aim is to have a spatial resolution of 1km x 1km, which is currently 10 x 10km. This high resolution is computationally not too demanding but very memory intensive. The memory bottleneck becomes especially apparent for data assimilation, for which we use OpenDA. OpenDa allows for different data assimilation techniques without the need to build these from scratch. We have developed a BMI adaptor for OpenDA, allowing OpenDA to use any BMI compatible model. To circumvent memory shortages which would result from standard applications of the Ensemble Kalman Filter, we have developed a variant that does not need to keep all ensemble members in working memory. At EGU, we will present this variant and how it fits well in HPC environments. An important step in the eWaterCycle project was the coupling between the hydrological and
The performance of composite forecast models of value-at-risk in the energy market
International Nuclear Information System (INIS)
Chiu, Yen-Chen; Chuang, I-Yuan; Lai, Jing-Yi
2010-01-01
This paper examines a comparative evaluation of the predictive performance of various Value-at-Risk (VaR) models in the energy market. This study extends the conventional research in literature, by proposing composite forecast models for applying to Brent and WTI crude oil prices. Forecasting techniques considered here include the EWMA, stable density, Kernel density, Hull and White, GARCH-GPD, plus composite forecasts from linearly combining two or more of the competing models above. Findings show Hull and White to be the most powerful approach for capturing downside risk in the energy market. Reasonable results are also available from carefully combining VaR forecasts. (author)
The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach
DEFF Research Database (Denmark)
Boldrini, Lorenzo; Hillebrand, Eric Tobias
We study the forecast power of the yield curve for macroeconomic time series, such as consumer price index, personal consumption expenditures, producer price index, real disposable income, unemployment rate, and industrial production. We employ a state-space model in which the forecasting objective...... is included in the state vector. This amounts to an augmented dynamic factor model in which the factors (level, slope, and curvature of the yield curve) are supervised for the macroeconomic forecast target. In other words, the factors are informed about the dynamics of the forecast objective. The factor...... loadings have the Nelson and Siegel (1987) structure and we consider one forecast target at a time. We compare the forecasting performance of our specification to benchmark models such as principal components regression, partial least squares, and ARMA(p,q) processes. We use the yield curve data from G...
Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms
Huang, Xin; Wang, Huaning; Xu, Long; Liu, Jinfu; Li, Rong; Dai, Xinghua
2018-03-01
Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
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Jun-He Yang
2017-01-01
Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
Directory of Open Access Journals (Sweden)
Luca Casolaro
2012-04-01
Full Text Available Questo lavoro presenta la stima di un modello econometrico per il credito bancario alle famiglie in Italia nel periodo 1984-2003. Il modello si compone di due equazioni: prestiti per l’acquisto di abitazioni e altri prestiti alle famiglie (tipicamente credito al consumo e finanziamenti in conto corrente. Entrambe le equazioni evidenziano uno stretto legame con l’andamento congiunturale dell’economia e il livello dei tassi di interesse reali; i mutui alle famiglie sono influenzati anche dalla dinamica del mercato immobiliare e di quello azionario. Il lavoro verifica l’esistenza di cambiamenti strutturali nelle equazioni in relazione alle trasformazioni intervenute nel mercato del credito nella seconda parte degli anni novanta e scompone il contributo alla crescita del credito alle famiglie tra i diversi fattori. This paper presents the estimation of an econometric model for bank lending to households in Italy in the period 1984-2003. The model consists of two equations: loans for house purchase and other loans to households (typically consumer credit and loans in the current account. Both equations show a close relationship with the economic trend and the level of real interest rates; household mortgages are also influenced by the dynamics of the housing market and the equity market. The work verifies the existence of structural changes in the equations in relation to the changes occurred in the credit market in the second half of the nineties and breaks down the contribution to growth in lending to households between the different factors. JEL Codes: D12, G21Keywords: credito bancario, famiglie, prestiti, modello econometrico
Almanaseer, Naser; Sankarasubramanian, A.; Bales, Jerad
2014-01-01
Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using
Can models help to forecast rainwater dynamics for rainfed ecosystem?
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Mukhtar Ahmed
2014-10-01
Full Text Available Simulation models are important tools to explore and illustrate dynamics of climatic variables in crop based ecosystem. In the rainfed ecosystem (RE, wheat production is impinged on certain climatic events per se high variability in rainfall and increased temperature. These climatic events turn out due to climatic drivers like Sea Surface Temperatures (SSTs and pressure. Current study is aimed to analyze long term rainfall data (1961–2011 of Pakistan׳s rainfed ecosystem zone (Islamabad, Chakwal and Talagang by using Agricultural Production Systems Simulator (APSIM and R model. The principal objective of this analysis was to study the link between SOI phases and SSTs; and thereby understanding the pattern of climate change due to these climatic drivers under rainfed conditions in Pakistan. The results revealed a positive link between July SOI phases and the rainfall variability during October–November (the sowing time of wheat in Pakistan. Long term rainfall data analysis (1961–2011 of Islamabad, Chakwal and Talagang revealed 44%, 40%, 35% possibility of exceeding median rainfall near zero whereas probability of consistently negative SOI phases were 35%, 34% and 33% respectively during July. Similarly, the forecasting results estimated by R using covariates like dry spell, NINO1.2, NINO3, NINO4, NINO3.4 and IOD of different months revealed that prediction of monsoon, wheat early growth, wheat grain filling period and total wheat growing season rainfall, have significant signals with climatic drivers. The study justified the importance of models in the decision making processes and rainfall forecasting as a beneficial and necessary tool for rainfed ecosystem conservation.
Retrospective forecast of ETAS model with daily parameters estimate
Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang
2016-04-01
We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.
Forecasting rain events - Meteorological models or collective intelligence?
Arazy, Ofer; Halfon, Noam; Malkinson, Dan
2015-04-01
Collective intelligence is shared (or group) intelligence that emerges from the collective efforts of many individuals. Collective intelligence is the aggregate of individual contributions: from simple collective decision making to more sophisticated aggregations such as in crowdsourcing and peer-production systems. In particular, collective intelligence could be used in making predictions about future events, for example by using prediction markets to forecast election results, stock prices, or the outcomes of sport events. To date, there is little research regarding the use of collective intelligence for prediction of weather forecasting. The objective of this study is to investigate the extent to which collective intelligence could be utilized to accurately predict weather events, and in particular rainfall. Our analyses employ metrics of group intelligence, as well as compare the accuracy of groups' predictions against the predictions of the standard model used by the National Meteorological Services. We report on preliminary results from a study conducted over the 2013-2014 and 2014-2015 winters. We have built a web site that allows people to make predictions on precipitation levels on certain locations. During each competition participants were allowed to enter their precipitation forecasts (i.e. 'bets') at three locations and these locations changed between competitions. A precipitation competition was defined as a 48-96 hour period (depending on the expected weather conditions), bets were open 24-48 hours prior to the competition, and during betting period participants were allowed to change their bets with no limitation. In order to explore the effect of transparency, betting mechanisms varied across study's sites: full transparency (participants able to see each other's bets); partial transparency (participants see the group's average bet); and no transparency (no information of others' bets is made available). Several interesting findings emerged from
Hyppolite, Judex; Trivedi, Pravin
2012-06-01
Cross-sectional latent class regression models, also known as switching regressions or hidden Markov models, cannot identify transitions between classes that may occur over time. This limitation can potentially be overcome when panel data are available. For such data, we develop a sequence of models that combine features of the static cross-sectional latent class (finite mixture) models with those of hidden Markov models. We model the probability of movement between categories in terms of a Markovian structure, which links the current state with a previous state, where state may refer to the category of an individual. This article presents a suite of mixture models of varying degree of complexity and flexibility for use in a panel count data setting, beginning with a baseline model which is a two-component mixture of Poisson distribution in which latent classes are fixed and permanent. Sequentially, we extend this framework (i) to allow the mixing proportions to be smoothly varying continuous functions of time-varying covariates, (ii) to add time dependence to the benchmark model by modeling the class-indicator variable as a first-order Markov chain and (iii) to extend item (i) by making it dynamic and introducing covariate dependence in the transition probabilities. We develop and implement estimation algorithms for these models and provide an empirical illustration using 1995-1999 panel data on the number of doctor visits derived from the German Socio-Economic Panel. Copyright © 2012 John Wiley & Sons, Ltd.
Electricity Demand Forecasting Using a Functional State Space Model
Nagbe , Komi; Cugliari , Jairo; Jacques , Julien
2018-01-01
In the last past years the liberalization of the electricity supply, the increase variability of electric appliances and their use, and the need to respond to the electricity demand in the real time had made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All this sources make hard electricity demand forecasting. To forecast the electr...
Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model
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José R. Andrade
2017-10-01
Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.
Data on photovoltaic power forecasting models for Mediterranean climate
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M. Malvoni
2016-06-01
The data sets, given in Supplementary material File 1, were used in DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015 [1] to compare Artificial Neural Networks and Least Square Support Vector Machines. It was found that LS-SVM with Wavelet Decomposition (WD outperforms ANN method. In DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016 [2] the same data were used for comparing different strategies for multi-step ahead forecast based on the hybrid Group Method of Data Handling networks and Least Square Support Vector Machine. The predicted PV power values by three models were reported in Supplementary material File 2.
Forecasting Foreign Institutional Investment Flows towards India Using ARIMA Modelling
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Vaishali S. Dhingra
2015-05-01
Full Text Available India has witnessed substantial increase in capital flows, particularly Foreign Institutional Investment in equity as well as derivatives segment since the 1990s. However, FII flows are sighted as ‘hot money’- more volatile than other type of flows, which gets affected by the domestic and global- macro economic factors, thereby raising questions about the need to encourage FII flows in narrow and shallow (in terms of absorption capacity capital market such as India. This paper attempts to forecast daily Aggregate FII flow in Indian Capital market and particularly in Futures Market (Derivative Segment using Auto Regressive Integrated Moving Average (ARIMA model.The paper tries to examine FII flows in India towards futures market along with spot market by tracing which AR terms and/or MA terms influence the current inflow or outflow.
MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH
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D. Tutberidze
2017-04-01
Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.