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A New Robust Inference for Predictive Quantile Regression

Zongwu Cai, Haiqiang Chen and Xiaosai Liao
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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

No 202002, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

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.

Keywords: Auxiliary regressor; Highly persistent predictor; Multiple regression; Predictive quantile regression; Robust inference; Weighted estimator (search for similar items in EconPapers)
JEL-codes: C22 C32 C58 G12 G14 (search for similar items in EconPapers)
Date: 2020-02, Revised 2020-02
New Economics Papers: this item is included in nep-cna, nep-ecm and nep-ets
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