Pooling of forecasts
AbstractWe consider forecasting using a combination, when no model coincides with a non-constant data generation process (DGP). Practical experience suggests that combining forecasts adds value, and can even dominate the best individual device. We show why this can occur when forecasting models are differentially mis-specified, and is likely to occur when the DGP is subject to location shifts. Moreover, averaging may then dominate over estimated weights in the combination. Finally, it cannot be proved that only non-encompassed devices should be retained in the combination. Empirical and Monte Carlo illustrations confirm the analysis. Copyright Royal Economic Socciety 2004
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Bibliographic InfoArticle provided by Royal Economic Society in its journal The Econometrics Journal.
Volume (Year): 7 (2004)
Issue (Month): 1 (06)
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Other versions of this item:
- David Hendry & Michael P. Clements, 2001. "Pooling of Forecasts," Economics Papers 2002-W9, Economics Group, Nuffield College, University of Oxford.
- David Hendry & Michael Clements, 2001. "Pooling of Forecasts," Economics Series Working Papers 2002-W09, University of Oxford, Department of Economics.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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