Jackknife inference with two-way clustering
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 and prove that they yield valid inferences asymptotically. 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 software package, twowayjack for Stata, that implements our recommended variance estimator.
Keywords
Cite
@article{arxiv.2406.08880,
title = {Jackknife inference with two-way clustering},
author = {James G. MacKinnon and Morten Ørregaard Nielsen and Matthew D. Webb},
journal= {arXiv preprint arXiv:2406.08880},
year = {2026}
}