Predictive quantile regressions under persistence and conditional heteroskedasticity

Rui Fan and Ji Hyung Lee

Journal of Econometrics, 2019, vol. 213, issue 1, 261-280

Abstract: This paper provides an improved inference for predictive quantile regressions with persistent predictors and conditionally heteroskedastic errors. The confidence intervals based on conventional quantile regression techniques are not valid when predictors are highly persistent. Moreover, the conditional heteroskedasticity introduces rather complicated nuisance parameters in the limit theory, whose estimation errors can be another source of distortion. We propose a size-corrected bootstrap inference thereby avoiding the nuisance parameter estimation. The bootstrap consistency is shown even with the nonstationary predictors and conditionally heteroskedastic innovations. Monte Carlo simulation confirms the significantly better test size performances of the new methods. The empirical exercises on stock return quantile predictability are revisited.

Keywords: α-mixing process; Conditional heteroskedasticity; Moving block bootstrap; Predictive regression; Quantile regression (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:213:y:2019:i:1:p:261-280

DOI: 10.1016/j.jeconom.2019.04.014

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