Tests of equal forecast accuracy for overlapping models
AbstractThis 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2011-024.
Date of creation: 2011
Date of revision:
Other versions of this item:
- Todd E. Clark & Michael W. Mccracken, 2014. "Tests Of Equal Forecast Accuracy For Overlapping Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 415-430, 04.
- Todd Clark & Michael W. McCracken, 2011. "Tests of equal forecast accuracy for overlapping models," Working Paper 1121, Federal Reserve Bank of Cleveland.
- NEP-ALL-2011-10-09 (All new papers)
- NEP-CBA-2011-10-09 (Central Banking)
- NEP-FOR-2011-10-09 (Forecasting)
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