The use of prior information in forecast combination

Abstract

Simple averages often, but not always, outperform more sophisticated “optimal” forecast composites. We used Bayesian shrinkage techniques to allow the incorporation of prior information into the estimation of combining weights; the estimated combining weights were coaxed or “shrunken” toward equality but were not forced to be exactly equal. The least-squares and prior (i.e., arithmetic average) weights then emerged as polar cases for the posterior mean; the exact location depended on prior precision, which was estimated from the data. In a simple example involving U.S. GNP forecasts, a large amount of shrinkage was found to be optimal.

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