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The Subcluster Wild Bootstrap for Few (Treated) Clusters

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Abstract
Inference based on cluster-robust standard errors is known to fail when the number of clusters is small, and the wild cluster bootstrap fails dramatically when the number of treated clusters is very small. We propose a family of new procedures called the subcluster wild bootstrap. In the case of pure treatment models, where all the observations in each cluster are either treated or not, the new procedures can work astonishingly well. The key requirement is that the sizes of the treated and untreated clusters should be very similar. Unfortunately, the analog of this requirement is not likely to hold for difference-in-differences regressions. Our theoretical results are supported by extensive simulations and an empirical example.

Suggested Citation

  • James G. MacKinnon & Matthew D. Webb, 2016. "The Subcluster Wild Bootstrap for Few (Treated) Clusters," Carleton Economic Papers 16-13, Carleton University, Department of Economics.
    • Handle: RePEc:car:carecp:16-13
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    File URL: http://www.carleton.ca/economics/wp-content/uploads/cep16-13.pdf
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    Cited by:

    1. Gröger, André, 2021. "Easy come, easy go? Economic shocks, labor migration and the family left behind," Journal of International Economics, Elsevier, vol. 128(C).

    More about this item

    Keywords

    CRVE; grouped data; clustered data; wild bootstrap; wild cluster bootstrap subclustering; treatment model; difference in differences; robust inference;
    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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