Jackknife Inference with Two-Way Clustering

James MacKinnon, Morten Nielsen and Matthew Webb

No 1516, Working Paper from Economics Department, Queen's University

Abstract: For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are often poor. We discuss several ways to improve inference with two-way clustering. Two of these are existing methods for avoiding, or at least ameliorating, the problem of undefined standard errors when a cluster-robust variance matrix estimator (CRVE) is not positive definite. One is a new method that always avoids the problem. More importantly, we propose a family of new two-wayCRVEs based on the cluster jackknife. Simulations for models with two-way fixed effects suggest that, in many cases, the cluster-jackknife CRVE combined with our new method yields surprisingly accurate inferences. We provide a simple software package, twowayjack for Stata, that implements our recommended variance estimator.

Keywords: cluster jackknife; cluster sizes; clustered data; cluster-robust variance estimator; CRVE; grouped data; two-way fixed effects (search for similar items in EconPapers)
JEL-codes: C10 C12 C21 C23 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2024-05
New Economics Papers: this item is included in nep-ifn
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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1516.pdf First version 2024 (application/pdf)

Related works:
Working Paper: Jackknife inference with two-way clustering (2024)
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