Forecasting Using Relative Entropy

FRB of Atlanta Working Paper No. 2002-22

33 Pages Posted: 10 Jan 2003

See all articles by John Robertson

John Robertson

Federal Reserve Bank of Atlanta

Ellis W. Tallman

Federal Reserve Banks - Federal Reserve Bank of Cleveland

Charles H. Whiteman

Pennsylvania State University - Smeal College of Business

Date Written: November 2002

Abstract

The paper describes a relative entropy procedure for imposing moment restrictions on simulated forecast distributions from a variety of models. Starting from an empirical forecast distribution for some variables of interest, the technique generates a new empirical distribution that satisfies a set of moment restrictions. The new distribution is chosen to be as close as possible to the original in the sense of minimizing the associated Kullback-Leibler Information Criterion, or relative entropy. The authors illustrate the technique by using several examples that show how restrictions from other forecasts and from economic theory may be introduced into a model's forecasts.

Keywords: approximate prior information, Kullback-Leibler Information Criterion, relative numerical efficiency

JEL Classification: E44, C53

Suggested Citation

Robertson, John C. and Tallman, Ellis W. and Whiteman, Charles H., Forecasting Using Relative Entropy (November 2002). FRB of Atlanta Working Paper No. 2002-22, Available at SSRN: https://ssrn.com/abstract=355460 or http://dx.doi.org/10.2139/ssrn.355460

John C. Robertson

Federal Reserve Bank of Atlanta ( email )

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Ellis W. Tallman (Contact Author)

Federal Reserve Banks - Federal Reserve Bank of Cleveland ( email )

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Cleveland, OH 44101-1387
United States

Charles H. Whiteman

Pennsylvania State University - Smeal College of Business

University Park, PA 16802
United States
814-863-0448 (Phone)

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