Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference

James G. MacKinnon*, Morten Ørregaard Nielsen, Matthew D. Webb

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, jackknife-based bootstrap data-generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially. Three empirical examples illustrate the new methods.

Original languageEnglish
JournalJournal of Applied Econometrics
Volume38
Issue5
Pages (from-to)671-694
Number of pages24
ISSN0883-7252
DOIs
Publication statusPublished - Aug 2023

Keywords

  • CRVE
  • cluster sizes
  • cluster-robust variance estimator
  • clustered data
  • grouped data
  • wild cluster bootstrap

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