Template-Type: ReDIF-Paper 1.0
Author-Name: A. Colin Cameron
Author-Name-First: A. Colin
Author-Name-Last: Cameron
Author-Name: Douglas L. Miller
Author-Name-First: Douglas L.
Author-Name-Last: Miller
Author-Workplace-Name: Department of Economics, University of California Davis
Title: Robust Inference with Clustered Data
Abstract: 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 significance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Duflo and Mullainathan (2004). We emphasize OLS estimation with statistical inference based on minimal assumptions regarding the error correlation process. Complications we consider include cluster-specific fixed effects, few clusters, multi-way clustering, more efficient feasible GLS estimation, and adaptation to nonlinear and instrumental variables estimators.
Length: 28
File-URL: https://repec.dss.ucdavis.edu/files/Q6ugdt6N6bxkmWrgu23gvxVv/10-7.pdf
File-Format: application/pdf
Number: 316
Classification-JEL: C12, C21, C23
KeyWords: Cluster robust, random effects, fixed effects, differences in differences, cluster bootstrap, few clusters, multi-way clusters.
Creation-Date: 20100405
Handle: RePEc:cda:wpaper:316