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Asymptotic Theory And Wild Bootstrap Inference With Clustered Errors

Author

Listed:
  • Antoine A. Djogbenou

    (Queen's University)

  • James G. MacKinnon

    (Queen's University)

  • Morten Ø. Nielsen

    (Queen's University and CREATES)

Abstract

We study asymptotic inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap and the ordinary wild bootstrap. We stateconditions under which both asymptotic and bootstrap tests and confidence intervals will be asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. To include power in the analysis, we allow the data to be generated under sequences of local alternatives. Under a somewhat stronger set of conditions, we also derive formal Edgeworth expansions for the asymptotic and bootstrap test statistics. Simulation experiments illustrate the theoretical results, and the Edgeworth expansions explain the overrejection of the asymptotic test and shed light on the choice of auxiliary distribution for the wild bootstrap.

Suggested Citation

  • Antoine A. Djogbenou & James G. MacKinnon & Morten Ø. Nielsen, 2018. "Asymptotic Theory And Wild Bootstrap Inference With Clustered Errors," Working Paper 1399, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1399
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    References listed on IDEAS

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    19. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
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    More about this item

    Keywords

    wild cluster bootstrap; clustered data; cluster-robust variance estimator; CRVE; Edgeworth expansion; inference; wild bootstrap;
    All these keywords.

    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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