Testing for the appropriate level of clustering in linear regression models

James MacKinnon, Morten Nielsen and Matthew Webb

No 1428, Working Paper from Economics Department, Queen's University

Abstract: The overwhelming majority of empirical research that uses cluster-robust inference assumes that the clustering structure is known, even though there are often several possible ways in which a dataset could be clustered. We propose two tests for the correct level of clustering in regression models. One test focuses on inference about a single coefficient, and the other on inference about two or more coefficients. We provide both asymptotic and wild bootstrap implementations. The proposed tests work for a null hypothesis of either no clustering or "fine" clustering against alternatives of "coarser" clustering. We also propose a sequential testing procedure to determine the appropriate level of clustering. Simulations suggest that the bootstrap tests perform very well under the null hypothesis and can have excellent power. An empirical example suggests that using the tests leads to sensible inferences.

Keywords: CRVE; grouped data; clustered data; cluster-robust variance estimator; robust inference; wild bootstrap; wild cluster bootstrap (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2022-12
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 (6)

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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1428.pdf Third version 2022 (application/pdf)

Related works:
Journal Article: Testing for the appropriate level of clustering in linear regression models (2023)
Working Paper: Testing for the appropriate level of clustering in linear regression models (2023)
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