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

James MacKinnon, Morten Nielsen and Matthew Webb

No 1485, Working Paper from Economics Department, Queen's University

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.

Keywords: bootstrap; clustered data; grouped data; cluster-robust variance estimator; CRVE; cluster sizes; wild cluster bootstrap (search for similar items in EconPapers)
JEL-codes: C10 C12 C21 C23 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2022-10
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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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 (2023)
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