Robust Inference with Clustered Data A

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical signi cance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Du o and Mullainathan (2004). We emphasize OLS estimation with statistical inference based on minimal assumptions regarding the error correlation process. Complications we consider include cluster-speci c xed effects, few clusters, multi-way clustering, more e cient feasible GLS estimation, and adaptation to nonlinear and instrumental variables estimators.