Robust inference with multi-way clustering
By: A. Colin Cameron, Jonah B. Gelbach, Douglas L. Miller
Contributors: Cameron, A.; Gelbach, Jonah B.; Miller, Douglas L.; National Bureau of Economic Research
Series: NBER working paper series -- working paper . 327; Working paper series (National Bureau of Economic Research : Online) -- working paper no. . 327.
Publisher: National Bureau of Economic Research
Published: 2006
Place of publication: Cambridge, MA
Language: eng
Format: [electronic resource] /
Description: "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"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 10/12/2006.

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