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Bootstrap Inference for Panel Data Quantile Regression

Author

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  • Antonio F. Galvao
  • Thomas Parker
  • Zhijie Xiao
Abstract
This article 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.

Suggested Citation

  • Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2024. "Bootstrap Inference for Panel Data Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 628-639, April.
    • Handle: RePEc:taf:jnlbes:v:42:y:2024:i:2:p:628-639
    DOI: 10.1080/07350015.2023.2210189
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    2. Li Tao & Lingnan Tai & Manling Qian & Maozai Tian, 2023. "A New Instrumental-Type Estimator for Quantile Regression Models," Mathematics, MDPI, vol. 11(15), pages 1-26, August.

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