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Robust Inference with Multi-way Clustering

A. Cameron, Jonah Gelbach and Douglas Miller ()

No 327, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM, that provcides cluster-robust inference when there is two-way or multi-way clustering that is non-nested. 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 et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where two-way clustering is present.

JEL-codes: C12 C21 C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2006-09
Note: TWP
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Journal Article: Robust Inference With Multiway Clustering (2011) Downloads
Journal Article: Robust Inference With Multiway Clustering (2011) Downloads
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