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Wild cluster bootstrap confidence intervals

Author

Listed:
  • James G. MacKinnon

    (Department of Economics, Queen’s University)

Abstract

Confidence intervals based on cluster-robust covariance matrices can be constructed in many ways. In addition to conventional intervals obtained by inverting Wald (t) tests, the paper studies intervals obtained by inverting LM tests, studentized bootstrap intervals based on the wild cluster bootstrap, and restricted bootstrap intervals obtained by inverting bootstrap Wald and LM tests. It also studies the choice of an auxiliary distribution for the wild bootstrap, a modified covariance matrix based on transforming the residuals that was proposed some years ago, and new wild bootstrap procedures based on the same idea. Some procedures perform extraordinarily well even with the number of clusters is small.

Suggested Citation

  • James G. MacKinnon, 2020. "Wild cluster bootstrap confidence intervals," L'Actualité Economique, Société Canadienne de Science Economique, vol. 96(4), pages 721-743.
    • Handle: RePEc:ris:actuec:0237
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    1. is not listed on IDEAS
    2. Podstawski, Maximilian & Velinov, Anton, 2018. "The state dependent impact of bank exposure on sovereign risk," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 88, pages 63-75.
    3. MacKinnon, James G., 2023. "Fast cluster bootstrap methods for linear regression models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 52-71.
    4. MacKinnon, James G. & Orregaard Nielsen, Morten & Webb, Matthew D., 2017. "Bootstrap and Asymptotic Inference with Multiway Clustering," Queen's Economics Department Working Papers 274712, Queen's University - Department of Economics.
    5. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2021. "Wild Bootstrap and Asymptotic Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 505-519, March.
    6. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    7. repec:osf:metaar:x6uhk_v1 is not listed on IDEAS
    8. François Gardes, 2021. "Biases on variances estimated on large data-sets," Documents de travail du Centre d'Economie de la Sorbonne 21022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    9. Podstawski, Maximilian & Velinov, Anton, 2018. "The state dependent impact of bank exposure on sovereign risk," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 63-75.
    10. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    11. James G. MacKinnon, 2019. "How cluster-robust inference is changing applied econometrics," Canadian Journal of Economics, Canadian Economics Association, vol. 52(3), pages 851-881, August.
    12. François Gardes, 2021. "Biases on variances estimated on large data-sets," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-03325118, HAL.
    13. Bartlett, Robert P. & McCrary, Justin, 2019. "How rigged are stock markets? Evidence from microsecond timestamps," Journal of Financial Markets, Elsevier, vol. 45(C), pages 37-60.
    14. Ritter, Joseph A., "undated". "Incentive effects of SNAP work requirements," Staff Papers 281156, University of Minnesota, Department of Applied Economics.
    15. Yu Zheng & Honggang Fan, 2025. "Fast Cluster Bootstrap Methods for Spatial Error Models," Mathematics, MDPI, vol. 13(18), pages 1-16, September.
    16. François Gardes, 2021. "Biases on variances estimated on large data-sets," Post-Print halshs-03325118, HAL.
    17. 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.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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