Optimal Combination of Survey Forecasts

21 Pages Posted: 28 Sep 2012

See all articles by Cristina Conflitti

Cristina Conflitti

affiliation not provided to SSRN

Christine De Mol

Free University of Brussels (VUB/ULB) - European Center for Advanced Research in Economics and Statistics (ECARES)

Domenico Giannone

Federal Reserve Banks - Federal Reserve Bank of New York; Centre for Economic Policy Research (CEPR)

Date Written: August 2012

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, high-dimensional data, real-time data, shrinkage, Survey of Professional Forecasters

JEL Classification: C22, C53

Suggested Citation

Conflitti, Cristina and De Mol, Christine and Giannone, Domenico, Optimal Combination of Survey Forecasts (August 2012). CEPR Discussion Paper No. DP9096. Available at SSRN: https://ssrn.com/abstract=2153546

Cristina Conflitti (Contact Author)

affiliation not provided to SSRN ( email )

Christine De Mol

Free University of Brussels (VUB/ULB) - European Center for Advanced Research in Economics and Statistics (ECARES) ( email )

Ave. Franklin D Roosevelt, 50 - C.P. 114
Brussels, B-1050
Belgium

Domenico Giannone

Federal Reserve Banks - Federal Reserve Bank of New York ( email )

33 Liberty Street
New York, NY 10045
United States

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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