Predictive ability tests with possibly overlapping models

Valentina Corradi, Jack Fosten and Daniel Gutknecht

Journal of Econometrics, 2024, vol. 241, issue 1

Abstract: This paper provides novel tests for comparing out-of-sample predictive ability of two or more competing models that are possibly overlapping. The tests do not require pre-testing, they allow for dynamic misspecification and are valid under different estimation schemes and loss functions. In pairwise model comparisons, the test is constructed by adding a random perturbation to both the numerator and denominator of a standard Diebold–Mariano test statistic. This prevents degeneracy in the presence of overlapping models but becomes asymptotically negligible otherwise. The test is shown to control the Type I error probability asymptotically at the nominal level, uniformly over all null data generating processes. A similar idea is used to develop a superior predictive ability test for the comparison of multiple models against a benchmark. Monte Carlo simulations demonstrate that our tests exhibit very good size control in finite samples reducing both over- and under-rejection relative to its competitors. Finally, an application to forecasting U.S. excess bond returns provides evidence in favour of models using macroeconomic factors.

Keywords: Degeneracy; Uniform inference; Block bootstrap; Out-of-sample evaluation; Excess bond returns (search for similar items in EconPapers)
JEL-codes: C12 C22 C53 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:241:y:2024:i:1:s0304407624000629

DOI: 10.1016/j.jeconom.2024.105716

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