In this issue we have: Forecasting economic activity for Estonia : The application of dynamic principal component analyses ; Taylor Rules and the Euro ; Forecasting VaR and Expected shortfall using dynamical Systems : a risk Management Strategy ; Combination of Forecast Methods Using Encompassing Tests. An Algorithm-Based Procedure.
|In this paper, the dynamic common factors method of Forni et al. (2000) is applied to a large panel of economic time series on the Estonian economy. In order to improve forecasting of economic activity in Estonia, we derive a leading indicator composed of the common components of twelve series, which were identified as leading. The resulting indicator performs better than two other indicators, which are based on a small-scale state-space model used by Stock and Watson (1991) and a large-scale static principal components model used by Stock and Watson (2002), respectively. It also clearly outperforms the naive benchmark in both in-sample and out-of-sample forecast comparisons|
|Keywords:||Estonia, forecasting, turning points, dynamic factor models, dynamic principal components, forecast performance|
|JEL:||C32 C33 C53 E37|
|This paper uses real-time data to analyze whether the variables that normally enter central banks' interest-rate-setting rules, which we call Taylor rule fundamentals, can provide evidence of out-of-sample predictability for the United States Dollar/Euro exchange rate from the inception of the Euro in 1999 to the end of 2007. The major result of the paper is that the null hypothesis of no predictability can be rejected against an alternative hypothesis of predictability with Taylor rule fundamentals for a wide variety of specifications that include inflation and a measure of real economic activity in the forecasting regression. We also present less formal evidence that, with real-time data, the Taylor rule provides a better description of ECB than of Fed policy during this period. While the evidence of predictability is only found for specifications that do not include the real exchange rate in the forecasting r! egression, the results are robust to whether or not the coefficients on inflation and the real economic activity measure are constrained to be the same for the U.S. and the Euro Area and to whether or not there is interest rate smoothing. The evidence of predictability is stronger for real-time than for revised data, about the same with inflation forecasts as with inflation rates, and weakens if output gap growth is included in the forecasting regression. Bad news about inflation and good news about real economic activity both lead to out-of-sample predictability through forecasted exchange rate appreciation.|
|Keywords:||Taylor rule; euro; exchange rate; forecasting; ECB; euro area.|
|JEL:||F37 E58 E52 F31|
|By:||Dominique Guegan (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I)
Cyril Caillault (FORTIS Investments – Fortis investments)
|Using non-parametric (copulas) 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 RiskMetric 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 – Copula – RiskMetrics – Risk management -GARCH models – Switching models.|
|By:||Costantini, Mauro (Department of Economics, University of Vienna BWZ, Vienna, Austria)
Pappalardo, Carmine (Institute for Studies and Economic Analysis (ISAE), Rome, Italy)
|This paper proposes a strategy to increase the efficiency of forecast combining methods. Given the availability of a wide range of forecasting models for the same variable of interest, our goal is to apply combining methods to a restricted set of models. To this aim, an algorithm procedure based on a widely used encompassing test (Harvey, Leybourne, Newbold, 1998) is developed. First, forecasting models are ranked according to a measure of predictive accuracy (RMSFE) and, in a consecutive step, each prediction is chosen for combining only if it is not encompassed by the competing models. To assess the robustness of this procedure, an empirical application to Italian monthly industrial production using ISAE short-term forecasting models is provided.|
|Keywords:||Combining forecasts, Econometric models, Evaluating forecasts, Models selection, Time series|
Taken from the NEP-FOR mailing list edited by Rob Hyndman.