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On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study

Author

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  • Antonio F. Galvao

    (Department of Economics, University of Iowa, W284 PBB, 21 E. Market Street, Iowa City, IA 52242, USA)

  • Gabriel Montes-Rojas

    (CONICET-Universidad de San Andrés, Vito Dumas 284, Victoria, B1644BID, Pcia. de Bs. As., Argentina
    Department of Economics, City University London, Northampton Square, London EC1V 0HB, UK)

Abstract

This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temporal resampling is performed from the time series. Finally, a more general resampling scheme, which considers sampling from both the cross-sectional and temporal dimensions, is introduced. The bootstrap algorithms are computationally attractive and easy to use in practice. We evaluate the performance of the bootstrap confidence interval by means of Monte Carlo simulations. The results show that the bootstrap methods have good finite sample performance for both location and location-scale models.

Suggested Citation

  • Antonio F. Galvao & Gabriel Montes-Rojas, 2015. "On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study," Econometrics, MDPI, vol. 3(3), pages 1-13, September.
    • Handle: RePEc:gam:jecnmx:v:3:y:2015:i:3:p:654-666:d:55584
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    References listed on IDEAS

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    7. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    8. Xiaowen Dai & Shidan Huang & Libin Jin & Maozai Tian, 2023. "Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients," Mathematics, MDPI, vol. 11(9), pages 1-16, April.
    9. Zhang, Yingying & Wang, Huixia Judy & Zhu, Zhongyi, 2019. "Quantile-regression-based clustering for panel data," Journal of Econometrics, Elsevier, vol. 213(1), pages 54-67.
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    11. Galina Besstremyannaya & Sergei Golovan, 2019. "Reconsideration of a simple approach to quantile regression for panel data: a comment on the Canay (2011) fixed effects estimator," Working Papers w0249, Center for Economic and Financial Research (CEFIR).
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    14. Galina Besstremyannaya & Sergei Golovan, 2019. "Reconsideration of a simple approach to quantile regression for panel data: a comment on the Canay (2011) fixed effects estimator," Working Papers w0249, New Economic School (NES).

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