Lee, JiHyung (2015): Predictive quantile regression with persistent covariates: IVXQR approach. Forthcoming in: Journal of Econometrics

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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 IVXQR methods correct the distortion arising from persistent multivariate predictors while preserving discriminatory power. Simulations confirm that IVXQR methods inherit the robust properties of QR. These methods are employed to examine the predictability of US stock returns at various quantile levels.
Item Type:  MPRA Paper 

Original Title:  Predictive quantile regression with persistent covariates: IVXQR approach 
Language:  English 
Keywords:  IVX filtering, Local to unity, Multivariate predictors, Predictive regression, Quantile regression. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes 
Item ID:  65150 
Depositing User:  JiHyung Lee 
Date Deposited:  21. Jun 2015 03:50 
Last Modified:  21. Jun 2015 04:39 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/65150 