Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2014-09-19
Number: 14-127/III
Author-Name: Gerda Claeskens
Author-Workplace-Name: KU Leuven, Belgium
Author-Name: Jan Magnus
Author-Workplace-Name: VU University Amsterdam, the Netherlands
Author-Name: Andrey Vasnev
Author-Workplace-Name: University of Sydney, Australia
Author-Name: Wendun Wang
Author-Workplace-Name: Erasmus University, Rotterdam, the Netherlands
Title: The Forecast Combination Puzzle: A Simple Theoretical Explanation
Abstract: This papers offers a theoretical explanation for the stylized fact that forecast combinations with estimated optimal weights often perform poorly in applications. The properties of the forecast combination are typically derived under the assumption that the weights are fixed, while in practice they need to be estimated. If the fact that the weights are random rather than fixed is taken into account during the optimality derivation, then the forecast combination will be biased (even when the original forecasts are unbiased) and its variance is larger than in the fixed-weights case. In particular, there is no guarantee that the 'optimal' forecast combination will be better than the equal-weights case or even improve on the original forecasts. We provide the underlying theory, some special cases and an application in the context of model selection.
Classification-JEL: C53, C52
Keywords: forecast combination, optimal weights, model selection
File-Url: https://papers.tinbergen.nl/14127.pdf
File-Format: application/pdf
File-Size: 141263 bytes
Handle: RePEc:tin:wpaper:20140127