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

James MacKinnon, Morten Nielsen and Matthew Webb

Papers from arXiv.org

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.

Date: 2023-01, Revised 2023-02
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Fast and reliable jackknife and bootstrap methods for cluster‐robust inference (2023)
Working Paper: Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference (2022)
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