Predictive quantile regression with persistent covariates: IVX-QR approach

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.