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

Citation Type: Journal Article

Publication Year: 2022

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Abstract: We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. These estimators have previously been com-putationally infeasible except for small samples. We also propose several new variants of the wild cluster bootstrap, which involve the new 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.

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Authors: MacKinnon, James G; Nielsen, Morten Ørregaard; Webb, Matthew D

Periodical (Full): Journal of Applied Econometrics

Issue:

Volume: 38

Pages: 671-694

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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