Tests of Equal Forecast Accuracy for Overlapping Models

31 Pages Posted: 12 Apr 2012

See all articles by Todd E. Clark

Todd E. Clark

Federal Reserve Bank of Cleveland

Michael W. McCracken

Federal Reserve Banks - Federal Reserve Bank of St. Louis

Multiple version iconThere are 2 versions of this paper

Date Written: September 1, 2011

Abstract

This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy when the models being compared are overlapping in the sense of Vuong (1989). Two models are overlapping when the true model contains just a subset of variables common to the larger sets of variables included in the competing forecasting models. We consider an out-of-sample version of the two-step testing procedure recommended by Vuong but also show that an exact one-step procedure is sometimes applicable. When the models are overlapping, we provide a simple-to-use fixed regressor wild bootstrap that can be used to conduct valid inference. Monte Carlo simulations generally support the theoretical results: the two-step procedure is conservative while the one-step procedure can be accurately sized when appropriate. We conclude with an empirical application comparing the predictive content of credit spreads to growth in real stock prices for forecasting U.S. real GDP growth.

Keywords: overlapping models, prediction, out-of-sample

JEL Classification: C53, C12, C52

Suggested Citation

Clark, Todd E. and McCracken, Michael W., Tests of Equal Forecast Accuracy for Overlapping Models (September 1, 2011). Available at SSRN: https://ssrn.com/abstract=2038982 or http://dx.doi.org/10.2139/ssrn.2038982

Todd E. Clark (Contact Author)

Federal Reserve Bank of Cleveland ( email )

P.O. Box 6387
Cleveland, OH 44101
United States
216-579-2015 (Phone)

Michael W. McCracken

Federal Reserve Banks - Federal Reserve Bank of St. Louis ( email )

411 Locust St
Saint Louis, MO 63011
United States

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