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

2023/01/11 by James G. MacKinnon, Morten Ørregaard Nielsen, MacKinnon, James G. +3 · 2 citations
Mathematics · #Advanced Statistical Methods and Models #Econometrics (econ.EM) #FOS: Economics and business #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2301.04527

openalex publication_date 2023/01/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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