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on Forecasting |
| By: | Katarzyna Maciejowska; Rafal Weron |
| Abstract: | We show that incorporating the intra-day relationships of electricity prices and trading volumes improves the accuracy of forecasts of daily electricity spot prices. We use half-hourly data from the UK power market to model the spot prices directly (via ARX and Vector ARX models) and indirectly (via factor models). The forecasting performance of five econometric models is evaluated and compared with that of a univariate model, which uses only (aggregated) daily data. The results indicate that there are forecast improvements from incorporating the disaggregated data, especially, when the forecast horizon exceeds one week. Additional improvements are achieved when the correlation structure of the intra-day relationships is explored. |
| Keywords: | Electricity spot price; Forecasting; Disaggregated data; Vector autoregression; Factor model; Principal components; |
| JEL: | C32 C38 C53 Q47 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:wuu:wpaper:hsc1301&r=for |
| By: | Joshua C.C. Chan; Eric Eisenstat |
| Abstract: | Empirical work in macroeconometrics has mostly restricted to using VARs, even though there are strong theoretical reasons to consider general VARMAs. This is perhaps because estimation of VARMAs is perceived to be challenging. In this article, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with stochastic volatility and time-varying parameters. We illustrate the methodology through a macroeconomic forecasting exercise. We show that VARMAs produce better density forecasts than VARs, particularly for short forecast horizons. |
| JEL: | C11 C32 C53 |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:acb:cbeeco:2013-604&r=for |
| By: | Issler, João Victor; Rodrigues, Claudia; Burjack, Rafael |
| Abstract: | The objective of this article is to study (understand and forecast) spotmetal price levels and changes at monthly, quarterly, and annual horizons.The data to be used consists of metal-commodity prices in a monthly frequencyfrom 1957 to 2012 from the International Financial Statistics of the IMF onindividual metal series. We will also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009) , whichare available for download. Regarding short- and long-run comovement, we willapply the techniques and the tests proposed in the common-feature literature tobuild parsimonious VARs, which possibly entail quasi-structural relationshipsbetween different commodity prices and/or between a given commodity priceand its potential demand determinants. These parsimonious VARs will be laterused as forecasting models to be combined to yield metal-commodity pricesoptimal forecasts. Regarding out-of-sample forecasts, we will use a variety of models (linear and non-linear, single equation and multivariate) and a varietyof co-variates to forecast the returns and prices of metal commodities. Withthe forecasts of a large number of models (N large) and a large number of timeperiods (T large), we will apply the techniques put forth by the common-featureliterature on forecast combinations. |
| Date: | 2013–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:fgv:epgewp:736&r=for |
| By: | Bent Flyvbjerg |
| Abstract: | A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk, based on theories of decision making under uncertainty which won the 2002 Nobel prize in economics. First, the paper documents inaccuracy and risk in project management. Second, it explains inaccuracy in terms of optimism bias and strategic misrepresentation. Third, the theoretical basis is presented for a promising new method called "reference class forecasting," which achieves accuracy by basing forecasts on actual performance in a reference class of comparable projects and thereby bypassing both optimism bias and strategic misrepresentation. Fourth, the paper presents the first instance of practical reference class forecasting, which concerns cost forecasts for large transportation infrastructure projects. Finally, potentials for and barriers to reference class forecasting are assessed. |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1302.3642&r=for |
| By: | Jakub Nowotarski; Jakub Tomczyk; Rafal Weron |
| Abstract: | We present the results of an extensive study on modeling and forecasting of the long-term seasonal component (LTSC) of electricity spot prices. We consider a vast array of models including linear regressions, monthly dummies, sinusoidal decompositions and wavelet smoothers. We find that in terms of forecasting EEX and Nord Pool spot prices up to a year ahead, wavelet-based models significantly outperform all considered piecewise constant and sine-based models. This result challenges the traditional approach to deseasonalize spot electricity prices by fitting monthly dummies or sinusoidal functions. |
| Keywords: | Electricity spot price; Forecasting; Seasonality; Monthly dummies; Sinusoidal decomposition; Wavelets; |
| JEL: | C45 C53 C80 Q47 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:wuu:wpaper:hsc1302&r=for |
| By: | Miguel Belmonte (Department of Economics, University of Strathclyde); Gary Koop (Department of Economics, University of Strathclyde) |
| Abstract: | This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA)in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in‡ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results. |
| Keywords: | Model switching, forecast combination, switching state space model, infl‡ation forecasting |
| JEL: | C11 C52 E37 E47 |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:str:wpaper:1302&r=for |
| By: | Gary Koop (Department of Economics, University of Strathclyde) |
| Abstract: | This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach. |
| Keywords: | Bayesian VAR; forecasting; time-varying coefficients; state-space model |
| JEL: | C11 C52 E27 E37 |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:str:wpaper:1303&r=for |
| By: | Roberto Astolfi; Luca Lorenzoni; Jillian Oderkirk |
| Abstract: | Concerns about health expenditure growth and its long-term sustainability have stimulated the development of health expenditure forecasting models in many OECD countries. This comparative analysis reviewed 25 models that were developed by, or used for, policy analysis by OECD member countries and other international organisations... |
| JEL: | H51 I12 J11 |
| Date: | 2012–10–31 |
| URL: | http://d.repec.org/n?u=RePEc:oec:elsaad:59-en&r=for |
| By: | Chia-Lin Chang (National Chung Hsing University); Michael McAleer (Erasmus University Rotterdam, Complutense University of Madrid, Kyoto University) |
| Abstract: | Experts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Science citations database (hereafter ISI) for the Business - Finance (hereafter Finance) category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to answer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PI-BETA (Papers Ignored - By Even The Authors), 2-year Self-citation Threshold Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are analysed for 21 highly-cited journals in Finance. The harmonic mean of the ranks of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A linear regression model is used to forecast expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative information about a journal. The robustness of the rankings is also analysed. |
| Keywords: | Expert scores; Journal quality; RAMs; Impact factor; IFI; C3PO; PI-BETA; STAR; Eigenfactor; Article Influence; h-index; harmonic mean; robustness |
| JEL: | C18 C81 C83 |
| Date: | 2013–02–18 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20130029&r=for |
| By: | Halina Kowalczyk (National Bank of Poland, Economic Institute); Tomasz Lyziak (National Bank of Poland, Economic Institute); Ewa Stanisławska (National Bank of Poland, Economic Institute) |
| Abstract: | In this paper we present the NBP Survey of Professional Forecasters introduced in 2011 by the National Bank of Poland. It is a new survey that allows analysis of macroeconomic forecasts of professional economists, including their probabilistic forecasts of CPI inflation, GDP growth and the NBP reference rate. In the paper we discuss in detail survey methodology, whose some elements are novel. It refers especially to the construction of probabilistic survey questions. Instead of declaring probabilities that in a certain horizon a given variable will be in pre-defined intervals, NBP SPF experts declare median and the limits of a 90-percent probability range between the 5th and 95th percentile of their subjective probability distributions. To present the benefits from the applied design of the NBP SPF, we describe the first results obtained from the NBP SPF. |
| JEL: | C82 D84 E52 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:nbp:nbpmis:142&r=for |