Jackknife inference with two-way clustering

James MacKinnon, Morten Nielsen and Matthew Webb

Papers from arXiv.org

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-way CRVEs 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.

Date: 2024-06
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http://arxiv.org/pdf/2406.08880 Latest version (application/pdf)

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Working Paper: Jackknife Inference with Two-Way Clustering (2024)
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