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Cluster-Robust Bootstrap Inference in Quantile Regression Models

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  • Andreas Hagemann

Abstract

In this article I develop a wild bootstrap procedure for cluster-robust inference in linear quantile regression models. I show that the bootstrap leads to asymptotically valid inference on the entire quantile regression process in a setting with a large number of small, heterogeneous clusters and provides consistent estimates of the asymptotic covariance function of that process. The proposed bootstrap procedure is easy to implement and performs well even when the number of clusters is much smaller than the sample size. An application to Project STAR data is provided. Supplementary materials for this article are available online.

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  • Andreas Hagemann, 2017. "Cluster-Robust Bootstrap Inference in Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 446-456, January.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:517:p:446-456
    DOI: 10.1080/01621459.2016.1148610
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    Cited by:

    1. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
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    3. Christian Zimpelmann & Hans-Martin von Gaudecker & Radost Holler & Lena Janys & Bettina Siflinger, 2021. "Drivers of Working Hours and Household Income Dynamics during the COVID-19 Pandemic: The Case of the Netherlands," ECONtribute Discussion Papers Series 093, University of Bonn and University of Cologne, Germany.
    4. Blasberg, Alexander & Kiesel, Rüdiger & Taschini, Luca, 2023. "Carbon default swap – disentangling the exposure to carbon risk through CDS," LSE Research Online Documents on Economics 118096, London School of Economics and Political Science, LSE Library.
    5. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    6. Liang Jiang & Xiaobin Liu & Peter C. B. Phillips & Yichong Zhang, 2020. "Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs," Papers 2005.11967, arXiv.org, revised May 2021.
    7. Wenjie Wang & Yichong Zhang, 2021. "Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters," Papers 2108.13707, arXiv.org, revised Jan 2024.
    8. Galina Besstremyannaya & Sergei Golovan, 2022. "Instrumental Variable Quantile Regression For Clustered Data," HSE Working papers WP BRP 255/EC/2022, National Research University Higher School of Economics.
    9. Zafar, Muhammad Wasif & Zaidi, Syed Anees Haider & Sinha, Avik & Gedikli, Ayfer & Hou, Fujun, 2019. "The role of stock market and banking sector development, and renewable energy consumption in carbon emissions: Insights from G-7 and N-11 countries," Resources Policy, Elsevier, vol. 62(C), pages 427-436.
    10. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2022. "Fast algorithms for the quantile regression process," Empirical Economics, Springer, vol. 62(1), pages 7-33, January.
    11. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
    12. Harding, Matthew & Kettler, Kyle & Lamarche, Carlos & Ma, Lala, 2023. "The (alleged) environmental and social benefits of dynamic pricing," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 574-593.
    13. Andreas Hagemann, 2023. "Inference on quantile processes with a finite number of clusters," Papers 2301.04687, arXiv.org, revised Jun 2023.
    14. Lamarche, Carlos & Parker, Thomas, 2023. "Wild bootstrap inference for penalized quantile regression for longitudinal data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1799-1826.
    15. Jungmo Yoon & Antonio F. Galvao, 2020. "Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects," Quantitative Economics, Econometric Society, vol. 11(2), pages 579-608, May.
    16. Matthew D. Webb, 2023. "Reworking wild bootstrap‐based inference for clustered errors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 56(3), pages 839-858, August.
    17. Jakob Peterlin & Nataša Kejžar & Rok Blagus, 2023. "Correct specification of design matrices in linear mixed effects models: tests with graphical representation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 184-210, March.
    18. Mullally, Conner C., 2018. "Livestock Transfers and Resilience: Evidence from a Randomized Trial in Guatemala," 2018 Annual Meeting, August 5-7, Washington, D.C. 274252, Agricultural and Applied Economics Association.
    19. Zimpelmann, Christian & Gaudecker, Hans-Martin von & Holler, Radost & Janys, Lena & Siflinger, Bettina, 2021. "Hours and income dynamics during the Covid-19 pandemic: The case of the Netherlands," Labour Economics, Elsevier, vol. 73(C).
    20. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    21. Blasberg, Alexander & Kiesel, Rüdiger & Taschini, Luca, 2023. "Carbon default swap – disentangling the exposure to carbon risk through CDS," LSE Research Online Documents on Economics 118092, London School of Economics and Political Science, LSE Library.

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