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Optimal Combination of Survey Forecasts

Cristina Conflitti (), Christine De Mol and Domenico Giannone ()

Working Papers ECARES from ULB -- Universite Libre de Bruxelles

Abstract: We consider the problem of optimally combining individual forecasts of gross domestic product (GDP) and inflation from the Survey of Professional Forecasters (SPF) dataset for the Euro Area. Contrary to the common practice of using equal combination weights, we compute optimal weights which minimize the mean square forecast error (MSFE) in the case of point forecasts and maximize a logarithmic score in the case of density forecasts. We show that this is a viable strategy even when the number of forecasts to combine gets large, provided we constrain these weights to be positive and to sum to one. Indeed, this enforces a form of shrinkage on the weights which ensures good out-of-sample performance of the combined forecasts.

Keywords: forecast combination; forecast evaluation; survey of professional forecasters; real-time data; shrinkage; high-dimensional data (search for similar items in EconPapers)
JEL-codes: C53 C22 (search for similar items in EconPapers)
Pages: 18 p.
Date: 2012-08
New Economics Papers: this item is included in nep-for
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Journal Article: Optimal combination of survey forecasts (2015) Downloads
Working Paper: Optimal Combination of Survey Forecasts (2012) Downloads
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