Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/301941 
Year of Publication: 
2024
Series/Report no.: 
Queen’s Economics Department Working Paper No. 1516
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
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.
Subjects: 
cluster jackknife
cluster sizes
clustered data
cluster-robust variance estimator
CRVE
grouped data
two-way fixed effects
JEL: 
C10
C12
C21
C23
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.