Predictive ability tests with possibly overlapping models

Valentina Corradi

doi:10.1016/j.jeconom.2024.105716

2024-04-01

PRE

Valentina Corradi *

Jack Fosten

Daniel Gutknecht

DOI：10.1016/j.jeconom.2024.105716

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摘要

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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.

Keyword:

Degeneracy

Uniform inference

Block bootstrap

Out-of-sample evaluation

Excess bond returns