Tests of equal forecast accuracy for overlapping models

Todd Clark and Michael McCracken

No 1121, Working Papers (Old Series) from Federal Reserve Bank of Cleveland

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: Forecasting (search for similar items in EconPapers)
Date: 2011
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: TESTS OF EQUAL FORECAST ACCURACY FOR OVERLAPPING MODELS (2014)
Working Paper: Tests of equal forecast accuracy for overlapping models (2011)
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