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Handbook of Economic Forecasting part 22

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Handbook of Economic Forecasting part 22. Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing | 184 A. Timmermann In their thick modeling approach Granger and Jeon 2004 recommend trimming five or ten percent of the worst models although the extent of the trimming will depend on the application at hand. More aggressive trimming has also been proposed. In a forecasting experiment involving the prediction of stock returns by means of a large set of forecasting models Aiolfi and Favero 2005 investigate the performance of a large set of trimming schemes. Their findings indicate that the best performance is obtained when the top 20 of the forecasting models is combined in the forecast so that 80 of the models ranked by their 2-value are trimmed. 7.4. Shrinkage often improves performance By and large shrinkage methods have performed quite well in empirical studies. In an empirical exercise containing four real-time forecasts of nominal and real GNP Diebold and Pauly 1990 report that shrinkage weights systematically improve upon the forecasting performance over methods that select a single forecast or use least squares estimates of the combination weights. They direct the shrinkage towards a prior reflecting equal weights and find that the optimal degree of shrinkage tends to be large. Similarly Stock and Watson 2004 find that shrinkage methods perform best when the degree of shrinkage towards equal weights is quite strong. Aiolfi and Timmermann 2006 explore persistence in the performance of forecasting models by proposing a set of combination strategies that first pre-select models into either quartiles or clusters on the basis of the distribution of past forecasting performance across models. Then they pool forecasts within each cluster and estimate optimal combination weights that are shrunk towards equal weights. These conditional combination strategies lead to better average forecasting performance than simpler strategies in common use such as using the single best model or averaging across all forecasting models or a small subset of these. Elliott 2004 .