Robust Inference With Multiway Clustering

A. Cameron, Jonah B. Gelbach and Douglas Miller

Journal of Business & Economic Statistics, 2011, vol. 29, issue 2, 238-249

Abstract: 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.

Date: 2011
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Citations: View citations in EconPapers (1380)

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Journal Article: Robust Inference With Multiway Clustering (2011)
Working Paper: Robust Inference with Multi-way Clustering (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:29:y:2011:i:2:p:238-249

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DOI: 10.1198/jbes.2010.07136

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