Robust Inference with Multi-way Clustering / A. Colin Cameron, Jonah B. Gelbach, Douglas L. Miller.

Format:

Book

Author/Creator:

Cameron, A. Colin.

Contributor:

National Bureau of Economic Research.

Gelbach, Jonah B.

Miller, Douglas L.

Series:

Technical Working Paper Series (National Bureau of Economic Research) no. t0327.

NBER technical working paper series no. t0327

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2006.

Summary:

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.

Notes:

Print version record

September 2006.