Robust Inference With Multiway Clustering

• Published in: Journal of business & economic statistics Vol. 29; no. 2; pp. 238 - 249
• Main Authors: Cameron, A. Colin, Gelbach, Jonah B., Miller, Douglas L.
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 01.04.2011; American Statistical Association; Taylor & Francis Ltd
• Subjects: Analytical estimating; Cluster-robust standard errors; Correlations; Economic models; Estimating techniques; Estimation methods; Estimators; Inference; Monte Carlo simulation; Standard error; Statistical estimation; Statistical inference; Statistical variance; Studies; Two-way clustering; Variance analysis; Working papers
• ISSN: 0735-0015; 1537-2707
• DOI: 10.1198/jbes.2010.07136

In this article we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM. This variance estimator enables cluster-robust inference when there is two-way or multiway clustering that is nonnested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g., Liang and Zeger 1986; Arellano 1987) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand, Duflo, and Mullainathan (2004) to two dimensions; and by application to studies in the empirical literature where two-way clustering is present.