Predictive quantile regression with persistent covariates: IVX-QR approach

Ji Hyung Lee

MPRA Paper from University Library of Munich, Germany

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: C1 C22 (search for similar items in EconPapers)
Date: 2015-04-28
New Economics Papers: this item is included in nep-ecm and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Predictive quantile regression with persistent covariates: IVX-QR approach (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:65150

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