Wild bootstrap inference for penalized quantile regression for longitudinal data

Carlos Lamarche and Thomas Parker

Journal of Econometrics, 2023, vol. 235, issue 2, 1799-1826

Abstract: The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator. The model puts no restrictions on individual effects, and the estimator achieves consistency by letting the shrinkage decay in importance asymptotically. The new method is easy to implement and simulation studies show that it has accurate small sample behavior in comparison with existing procedures. Finally, we illustrate the new approach using U.S. Census data to estimate a model that includes more than eighty thousand parameters.

Keywords: Quantile regression; Panel data; Penalized estimator; Bootstrap inference (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Wild Bootstrap Inference for Penalized Quantile Regression for Longitudinal Data (2022)
Working Paper: WILD BOOTSTRAP INFERENCE FOR PENALIZED QUANTILE REGRESSION FOR LONGITUDINAL DATA (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:1799-1826

DOI: 10.1016/j.jeconom.2022.11.011

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