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A New Robust Inference for Predictive Quantile Regression

Author

Listed:
  • Zongwu Cai

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

  • Haiqiang Chen

    (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China)

  • Xiaosai Liao

    (Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, China)

Abstract

This paper studies asset return predictability via quantile regression for all types of persistent regressors. We propose to estimating a quantile regression with an auxiliary regressor and constructing a weighted estimator using the estimated coefficients of the original predictor and the auxiliary regressor, together with a novel test procedure. We show that it can reach the local power under the different optimal rates for nonstationary and stationary predictors, respectively. Our approach can be easily implemented to test the joint predictive ability of financial variables in multiple regression. The heterogenous predictability of US stock returns at different quantile levels is reexamined.

Suggested Citation

  • Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    • Handle: RePEc:kan:wpaper:202002
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    File URL: http://www2.ku.edu/~kuwpaper/2020Papers/202002.pdf
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    References listed on IDEAS

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    Cited by:

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    3. Fukang Zhu & Mengya Liu & Shiqing Ling & Zongwu Cai, 2020. "Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202021, University of Kansas, Department of Economics, revised Dec 2020.

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    More about this item

    Keywords

    Auxiliary regressor; Highly persistent predictor; Multiple regression; Predictive quantile regression; Robust inference; Weighted estimator;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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