Reality checks and nested forecast model comparisons

Todd Clark and Michael McCracken

No 2010-032, Working Papers from Federal Reserve Bank of St. Louis

Abstract: This paper develops a novel and effective bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. The bootstrap, which combines elements of fixed regressor and wild bootstrap methods, is simple to use. We first derive the asymptotic distributions of tests of equal forecast accuracy and encompassing applied to forecasts from multiple models that nest the benchmark model ? that is, reality check tests applied to nested models. We then prove the validity of the bootstrap for these tests. Monte Carlo experiments indicate that our proposed bootstrap has better finite-sample size and power than other methods designed for comparison of non-nested models. We conclude with empirical applications to multiple-model forecasts of commodity prices and GDP growth.

Keywords: Economic; forecasting (search for similar items in EconPapers)
Date: 2010
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedlwp:2010-032

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DOI: 10.20955/wp.2010.032

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