Predictive quantile regressions under persistence and conditional heteroskedasticity

Rui Fan, JiHyung Lee

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalJournal of Econometrics
DOIs
StateAccepted/In press - Apr 11 2019

Keywords

  • α-mixing process
  • Conditional heteroskedasticity
  • Moving block bootstrap
  • Predictive regression
  • Quantile regression

ASJC Scopus subject areas

  • Economics and Econometrics

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