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Title:Robust inference with multi-way clustering PDF Logo
Authors:Miller, Douglas L.
Cameron, A. Colin
Gelbach, Jonah
Issue Date:2009
Series/Report no.:Working Papers, University of California, Department of Economics 09,9
Abstract:In this paper 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 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 oþer 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 eþects example of Bertrand et al. (2004) to two dimensions; and by application to studies in the empirical literature where two-way clustering is present.
Subjects:cluster-robust standard errors
two-way clustering
multi-way clustering
JEL:C12
C21
C23
Document Type:Working Paper
Appears in Collections:Working Papers, Department of Economics, UC Davis

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