Reality Checks and Comparisons of Nested Predictive Models

Todd E. Clark · Michael W. McCracken

87 citations0/0 files storedJournal of Business and Economic Statistics

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Forecasting Techniques and Applications Statistical Methods and Inference Advanced Statistical Methods and Models

Abstract

This article develops a simple bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. Our method combines elements of fixed regressor and wild bootstraps. 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. 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 nonnested models. Supplementary materials are available online.

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1994Comparing Predictive Accuracy 1995Comparing Predictive Accuracy 2007A Comprehensive Look at The Empirical Performance of Equity Premium Prediction 2006Approximately normal tests for equal predictive accuracy in nested models 2006Tests of Conditional Predictive Ability 1999Predictive regressions 1996Asymptotic Inference about Predictive Ability 1999Forecasting inflation

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