Inference in predictive quantile regressions

Alex Maynard, Katsumi Shimotsu and Nina Kuriyama

Journal of Econometrics, 2024, vol. 245, issue 1

Abstract: This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t-statistic in terms of functionals of Ornstein–Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has a reliable size in small samples and good power. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors – the dividend price ratio, earnings price ratio, and book-to-market ratio – to predict the median, shoulders, and tails of the stock return distribution.

Keywords: Local-to-unity; Quantile regression; Bonferroni method; Predictability; Stock return (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407624002203
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Inference in Predictive Quantile Regressions (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:245:y:2024:i:1:s0304407624002203

DOI: 10.1016/j.jeconom.2024.105875

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().