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Predictive ability tests with possibly overlapping models

Corradi, V., Fosten, J. & Gutknecht, D. (2024). Predictive ability tests with possibly overlapping models. Journal of Econometrics, 241(1), article number 105716. doi: 10.1016/j.jeconom.2024.105716


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.

Publication Type: Article
Additional Information: © 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Degeneracy, Uniform inference, Block bootstrap, Out-of-sample evaluation, Excess bond returns
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HG Finance
Departments: Bayes Business School
Bayes Business School > Finance
SWORD Depositor:
[thumbnail of ssrn-4375650.pdf] Text - Accepted Version
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