Cluster-robust inference: A guide to empirical practice

James MacKinnon, Morten Nielsen and Matthew Webb

Journal of Econometrics, 2023, vol. 232, issue 2, 272-299

Abstract: Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed. In this paper, we use these theoretical results to provide a guide to empirical practice. We do not attempt to present a comprehensive survey of the (very large) literature. Instead, we bridge theory and practice by providing a thorough guide on what to do and why, based on recently available econometric theory and simulation evidence. To practice what we preach, we include an empirical analysis of the effects of the minimum wage on labor supply of teenagers using individual data.

Keywords: Clustered data; Cluster jackknife; Cluster-robust variance estimator (CRVE); Robust inference; Wild cluster bootstrap (search for similar items in EconPapers)
JEL-codes: C12 C15 C21 C23 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (49)

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Related works:
Working Paper: Cluster-Robust Inference: A Guide to Empirical Practice (2022)
Working Paper: Cluster-Robust Inference: A Guide to Empirical Practice (2022)
Working Paper: Cluster-Robust Inference: A Guide to Empirical Practice (2022)
Working Paper: Cluster–robust inference: A guide to empirical practice (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:232:y:2023:i:2:p:272-299

DOI: 10.1016/j.jeconom.2022.04.001

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