Bootstrap inference for panel data quantile regression

Antonio Galvao, Thomas Parker and Zhijie Xiao

Papers from arXiv.org

Abstract: This paper develops bootstrap methods for practical statistical inference in panel data quantile regression models with fixed effects. We consider random-weighted bootstrap resampling and formally establish its validity for asymptotic inference. The bootstrap algorithm is simple to implement in practice by using a weighted quantile regression estimation for fixed effects panel data. We provide results under conditions that allow for temporal dependence of observations within individuals, thus encompassing a large class of possible empirical applications. Monte Carlo simulations provide numerical evidence the proposed bootstrap methods have correct finite sample properties. Finally, we provide an empirical illustration using the environmental Kuznets curve.

Date: 2021-11
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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http://arxiv.org/pdf/2111.03626 Latest version (application/pdf)

Related works:
Journal Article: Bootstrap Inference for Panel Data Quantile Regression (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2111.03626

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