Journal Article

Robust Inference With Multiway Clustering

Cameron, A. Colin, Miller, Douglas L., Gelbach, Jonah B.

2011

Overview

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.

• Publisher: Taylor & Francis,American Statistical Association,Taylor & Francis Ltd
• Subject: 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