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Predictive quantile regression with persistent covariates: IVX-QR approach

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  • Lee, JiHyung

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.

Suggested Citation

  • Lee, JiHyung, 2015. "Predictive quantile regression with persistent covariates: IVX-QR approach," MPRA Paper 65150, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:65150
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    References listed on IDEAS

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    More about this item

    Keywords

    IVX filtering; Local to unity; Multivariate predictors; Predictive regression; Quantile regression.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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