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A NOVEL APPROACH TO PREDICTIVE ACCURACY TESTING IN NESTED ENVIRONMENTS

Published online by Cambridge University Press:  17 May 2023

Jean-Yves Pitarakis Open the ORCID record for Jean-Yves Pitarakis [Opens in a new window]
Jean-Yves Pitarakis*
Affiliation:
University of Southampton
*
Address correspondence to Jean-Yves Pitarakis, Department of Economics, University of Southampton, Southampton SO17 1BJ, United Kingdom; e-mail: j.pitarakis@soton.ac.uk.
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Abstract

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We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample mean squared error loss differentials between the two competing models, leads to nuisance parameter-free Gaussian asymptotics, and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g., stationary and local to unit root). A local power analysis also establishes their ability to detect departures from the null in both stationary and persistent settings. Simulations calibrated to common economic and financial applications indicate that our methods have strong power with good size control across commonly encountered sample sizes.

Type
ARTICLES
Information
Econometric Theory , First View , pp. 1 - 44
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Footnotes

I wish to thank the Editor, Co-Editor, and three anonymous referees for the quality of the reports I have received and their in-depth review of an earlier version of this paper. I also wish to thank the ESRC for its financial support via grant ES/W000989/1. Any errors are my own responsibility.

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