publication . Preprint . 2009

Robust Inference with Multi-way Clustering

A. Colin Cameron; Jonah B. Gelbach; Douglas L. Miller; Doug Miller;
Open Access
  • Published: 01 May 2009
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 offer cluster-robust standard errors when there is one-way clustering. The method ...
Subjects
free text keywords: cluster-robust standard errors; two-way clustering; multi-way clustering., jel:C12, jel:C21, jel:C23
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