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Simple Tests for Stock Return Predictability with Good Size and Power Properties

Harvey, David I; Leybourne, Stephen J; Taylor, A M Robert

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Authors

DAVID HARVEY dave.harvey@nottingham.ac.uk
Professor of Econometrics

A M Robert Taylor



Abstract

We develop easy-to-implement tests for return predictability which, relative to extant tests in the literature, display attractive finite sample size control and power across a wide range of persistence and endogeneity levels for the predictor. Our approach is based on the standard regression t-ratio and a variant where the predictor is quasi-GLS (rather than OLS) demeaned. In the strongly persistent near-unit root environment, the limiting null distributions of these statistics depend on the endogeneity and local-to-unity parameters characterising the predictor. Analysis of the asymptotic local power functions of feasible implementations of these two tests, based on asymptotically conservative critical values, motivates a switching procedure between the two, employing the quasi-GLS demeaned variant unless the magnitude of the estimated endogeneity correlation parameter is small. Additionally, if the data suggests the predictor is weakly persistent, our approach switches into the standard t-ratio test with reference to standard normal critical values.

Citation

Harvey, D. I., Leybourne, S. J., & Taylor, A. M. R. (2021). Simple Tests for Stock Return Predictability with Good Size and Power Properties. Journal of Econometrics, 224(1), 198-214. https://doi.org/10.1016/j.jeconom.2021.01.004

Journal Article Type Article
Acceptance Date Jan 31, 2021
Online Publication Date Feb 23, 2021
Publication Date 2021-09
Deposit Date Feb 3, 2021
Publicly Available Date Mar 28, 2024
Journal Journal of Econometrics
Print ISSN 0304-4076
Electronic ISSN 1872-6895
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 224
Issue 1
Pages 198-214
DOI https://doi.org/10.1016/j.jeconom.2021.01.004
Keywords predictive regression; persistence; endogeneity; quasi-GLS demeaning; unit root test; hybrid statistic JEL classification: C12, C22
Public URL https://nottingham-repository.worktribe.com/output/5289900
Publisher URL https://www.sciencedirect.com/science/article/pii/S0304407621000270

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