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Wild Bootstrap Randomization Inference for Few Treated Clusters

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  • MacKinnon, James G.
  • Webb, Matthew D.

Abstract

When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator (CRVE) can severely over- reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not too small, they can either over-reject or under- reject seriously when it is. In a previous paper, we showed that procedures based on randomization inference (RI) can work well in such cases. However, RI can be imprac- tical when the number of clusters is small. We propose a bootstrap-based alternative to randomization inference, which mitigates the discrete nature of RI P values in the few-clusters case.

Suggested Citation

  • MacKinnon, James G. & Webb, Matthew D., 2018. "Wild Bootstrap Randomization Inference for Few Treated Clusters," Queen's Economics Department Working Papers 274730, Queen's University - Department of Economics.
    • Handle: RePEc:ags:quedwp:274730
    DOI: 10.22004/ag.econ.274730
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