Forecasting papers 2009-08-05

In this issue we have Predicting Elections from Biographical Information about Candidates, Forecasting electricity spot market prices with a k-factor GIGARCH process, Does money matter in inflation forecasting? Forecasting VaR and Expected Shortfall using Dynamical Systems, and more.

  • Predicting Elections from Biographical Information about Candidates
    Date: 2009-06-23
    By: Armstrong, J. Scott
    Graefe, Andreas
    Using the index method, we developed the PollyBio model to predict election outcomes. The model, based on 49 cues about candidates' biographies, was used to predict the outcome of the 28 U.S. presidential elections from 1900 to 2008. In using a simple heuristic, it correctly predicted the winner for 25 of the 28 elections and was wrong three times. In predicting the two-party vote shares for the last four elections from 1996 to 2008, the model's out-of-sample forecasts yielded a lower forecasting error than 12 benchmark models. By relying on different information and including more variables than traditional models, PollyBio improves on the accuracy of election forecasting. It is particularly helpful for forecasting open-seat elections. In addition, it can help parties to select the candidates running for office.
    Keywords: forecasting; unit weighting; Dawes rule; differential weighting
    JEL: C53
  • Forecasting electricity spot market prices with a k-factor GIGARCH process
    Date: 2009-04
    By: Abdou Kâ Diongue (UFR SAT – Université Gaston Berger – Université Gaston Berger de Saint-Louis)
    Dominique Guegan (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I, EEP-PSE – Ecole d'Économie de Paris – Paris School of Economics – Ecole d'Économie de Paris)
    Bertrand Vignal (EDF – EDF – Recherche et Développement)
    In this article, we investigate conditional mean and variance forecasts using a dynamic model following a k-factor GIGARCH process. We are particularly interested in calculating the conditional variance of the prediction error. We apply this method to electricity prices and test spot prices forecasts until one month ahead forecast. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria.
    Keywords: Conditional mean – conditional variance – forecast – electricity prices – GIGARCH process
  • Does money matter in inflation forecasting?
    Date: 2009
    By: Jane M. Binner
    Peter Tino
    Jonathan Tepper
    Richard G. Anderson
    Barry Jones
    Graham Kendall
    This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression – techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much suppor! t for the usefulness of monetary aggregates in forecasting inflation.
    Keywords: Forecasting ; Inflation (Finance) ; Monetary theory
  • Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy
    Date: 2009-04
    By: Cyril Caillault (Fortis Investments – Fortis investments)
    Dominique Guegan (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I, EEP-PSE – Ecole d'Économie de Paris – Paris School of Economics – Ecole d'Économie de Paris)
    Using non-parametric and parametric models, we show that the bivariate distribution of an Asian portfolio is not stable along all the period under study. We suggest several dynamic models to compute two market risk measures, the Value at Risk and the Expected Shortfall: the RiskMetrics methodology, the Multivariate GARCH models, the Multivariate Markov-Switching models, the empirical histogram and the dynamic copulas. We discuss the choice of the best method with respect to the policy management of bank supervisors. The copula approach seems to be a good compromise between all these models. It permits taking financial crises into account and obtaining a low capital requirement during the most important crises.
    Keywords: Value at Risk ; Expected Shortfall ; Copulas ; Risk management ; GARCH models ; Markov switching models
  • Irrational Bias in Inflation Forecasts
    Date: 2009-07-23
    By: Kim , Insu
    Kim, Minsoo
    This paper investigates the issue of rational expectations using inflation forecasts from the Survey of Professional Forecasters (SPF) and the Green Book. We provide an alternative test of rational expectations hypothesis by measuring the degree of persistence of potential systematic mistakes. The test is obtained by solving a signal extraction problem that distinguishes between systematic and non-systematic forecast errors. The findings indicate highly persistent systematic mistakes, which are driven by the inefficient use of available information, and reject the rational expectations hypothesis. The estimated time-varying bias can be used to improve the SPF and Green Book inflation forecast performance by at least 13.4%. This paper also documents evidence that the real interest rate plays a crucial role in explaining the level of bias that leads to under- and over predictions of actual inflation.
    Keywords: Inflation Expectations; Bias; Forecasts; Rational Expectations.
    JEL: D84
  • Can Parameter Instability Explain the Meese-Rogoff Puzzle?
    Date: 2009-07
    By: Philippe Bacchetta
    Eric van Wincoop
    Toni Beutler
    The empirical literature on nominal exchange rates shows that the current exchange rate is often a better predictor of future exchange rates than a linear combination of macroeconomic fundamentals. This result is behind the famous Meese-Rogoff puzzle. In this paper we evaluate whether parameter instability can account for this puzzle. We consider a theoretical reduced-form relationship between the exchange rate and fundamentals in which parameters are either constant or time varying. We calibrate the model to data for exchange rates and fundamentals and conduct the exact same Meese-Rogoff exercise with data generated by the model. Our main finding is that the impact of time-varying parameters on the prediction performance is either very small or goes in the wrong direction. To help interpret the findings, we derive theoretical results on the impact of time-varying parameters on the out-of-sample forecasting performance o! f the model. We conclude that it is not time-varying parameters, but rather small sample estimation bias, that explains the Meese-Rogoff puzzle.
    Keywords: exchange rate forecasting; time-varying coefficients
    JEL: F31
  • A New Data Set on Monetary Policy: The Economic Forecasts of Individual Members of the FOMC
    Date: 2009-07
    By: David H. Romer
    This paper describes a new data set of the forecasts of output growth, inflation, and unemployment prepared by individual members of the Federal Open Market Committee. The paper discusses the scope of the data set, possibilities for extending it, and some potential uses. It offers a preliminary examination of some of the cross-sectional features of the data.
    JEL: E52
  • Forecasting local climate for policy analysis : a pilot application for Ethiopia
    Date: 2009-07-01
    By: Blankespoor, Brian
    Pandey, Kiran Dev
    Wheeler, David
    This paper describes an approach to forecasting future climate at the local level using historical weather station and satellite data and future projections of climate data from global climate models (GCMs) that is easily understandable by policymakers and planners. It describes an approach to synthesize the myriad climate projections, often with conflicting messages, into an easily-interpreted set of graphical displays that summarizes the basic implications of the ensemble of available climate models. The method described in the paper can be applied to publicly-available data for any country and for any number of climate models. It does not depend on geographic scale and can be applied at the subnational, national, or regional level. The paper illustrates the results for future climate for Ethiopia using future climate scenarios projects by 8 global climate models. The graphical displays of nine possible future climate ! regimes (average temperature, precipitation and their seasonal distribution) for each grid-cell about 50km X 50 km). It also provides the probability associated with each of the nine-climate regimes.
    Keywords: Climate Change,Global Environment Facility,Water Conservation,Information
  • Taken from the NEP-FOR mailing list by Rob Hyndman.