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Wild Bootstrap Inference For Wildly Different Cluster Sizes

Author

Listed:
  • James G. MacKinnon

    (Queen's University)

  • Matthew D. Webb

    (Carleton University)

Abstract

The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the "rule of 42" is not true for unbalanced clusters. Rejection frequencies are higher for datasets with 50 clusters proportional to U.S. state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the "effective number" of clusters, and simulate placebo laws with dummy variable regressors.

Suggested Citation

  • James G. MacKinnon & Matthew D. Webb, 2015. "Wild Bootstrap Inference For Wildly Different Cluster Sizes," Working Paper 1314, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1314
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    CRVE; grouped data; clustered data; panel data; wild cluster bootstrap; placebo laws; effective number of clusters; bootstrap failure; difference in differences;
    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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