How cluster-robust inference is changing applied econometrics

James MacKinnon

No 1413, Working Paper from Economics Department, Queen's University

Abstract: In many fields of economics, and also in other disciplines, it is hard to justify the assumption that the random error terms in regression models are uncorrelated. It seems more plausible to assume that they are correlated within clusters, such as geographical areas or time periods, but uncorrelated across clusters. It has therefore become very popular to use "clustered" standard errors, which are robust against arbitrary patterns of within-cluster variation and covariation. Conventional methods for inference using clustered standard errors work very well when the model is correct and the data satisfy certain conditions, but they can produce very misleading results in other cases. This paper discusses some of the issues that users of these methods need to be aware of.

Keywords: clustered data; cluster-robust variance estimator; CRVE; wild cluster bootstrap; robust inference (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2019-03
New Economics Papers: this item is included in nep-ecm and nep-ets
References: Add references at CitEc
Citations: View citations in EconPapers (29)

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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1413.pdf First version 2019 (application/pdf)

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Journal Article: How cluster-robust inference is changing applied econometrics (2019)
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