Predictive quantile regression with persistent covariates: IVX-QR approach

Ji Hyung Lee

Journal of Econometrics, 2016, vol. 192, issue 1, 105-118

Abstract: This paper develops econometric methods for inference and prediction in quantile regression (QR) allowing for persistent predictors. Conventional QR econometric techniques lose their validity when predictors are highly persistent. I adopt and extend a methodology called IVX filtering (Magdalinos and Phillips, 2009) that is designed to handle predictor variables with various degrees of persistence. The proposed IVX-QR methods correct the distortion arising from persistent multivariate predictors while preserving discriminatory power. Simulations confirm that IVX-QR methods inherit the robust properties of QR. These methods are employed to examine the predictability of US stock returns at various quantile levels.

Keywords: IVX filtering; Local to unity; Multivariate predictors; Predictive regression; Quantile regression (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (48)

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Working Paper: Predictive quantile regression with persistent covariates: IVX-QR approach (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:192:y:2016:i:1:p:105-118

DOI: 10.1016/j.jeconom.2015.04.003

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