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Reworking Wild Bootstrap Based Inference for Clustered Errors

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  • Matthew D. Webb

    () (University of Calgary)

Abstract

Many empirical projects involve estimation with clustered data. While estimation is straightforward, reliable inference can be challenging. Past research has suggested a number of bootstrap procedures when there are few clusters. I demonstrate, using Monte Carlo experiments, that these bootstrap procedures perform poorly with fewer than eleven clusters. With few clusters, the wild cluster bootstrap results in p-values that are not point identified. I suggest two alternative wild bootstrap procedures. Monte Carlo simulations provide evidence that a 6-point bootstrap weight distribution improves the reliability of inference. A brief empirical example concerning education tax credits highlights the implications of these findings.

Suggested Citation

  • Matthew D. Webb, 2014. "Reworking Wild Bootstrap Based Inference for Clustered Errors," Working Papers 1315, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:1315
    as

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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1315.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    CRVE; grouped data; clustered data; panel data; wild bootstrap; wild cluster bootstrap; difference in differences; placebo laws;

    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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