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Robust Inference for Time Series Quantile Regression: A Dependent Wild Bootstrap-Based Approach

Zongwu Cai and Wei Long
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Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA
Wei Long: Department of Economics, Tulane University, New Orleans, LA 70118, USA

No 202612, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: Quantile regression is widely used to study heterogeneous effects, but inference in time series settings remains challenging when regression errors are serially correlated. Building on the dependent wild bootstrap, we develop an inference procedure for linear time series quantile regression that reweights the restricted quantile score with tapered multipliers and employs a one-step bootstrap update together with the HAC-based studentization. The procedure avoids repeated solution of a non-smooth quantile regression problem within each bootstrap draw while targeting the same inferential object as robust HAC testing. Under strong mixing and standard smoothness and bandwidth conditions, we establish asymptotic validity of the bootstrap test and derive its local power under Pitman alternatives. Monte Carlo results indicate improved size control relative to conventional and robust HAC methods, especially under strong dependence, with only modest differences in power. An application to the determinants of U.S. housing prices over the past four decades illustrates the practical usefulness of the method.

Keywords: Time series quantile regression; Dependent wild bootstrap; HAC inference (search for similar items in EconPapers)
JEL-codes: C12 C22 C46 (search for similar items in EconPapers)
Date: 2026-04, Revised 2026-04
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Persistent link: https://EconPapers.repec.org/RePEc:kan:wpaper:202612

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