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Robust Inference with Clustered Data

A. Cameron and Douglas Miller ()

No 318, Working Papers from University of California, Davis, Department of Economics

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.

Keywords: Cluster robust; random e ects; xed e ects; di erences in di erences; cluster bootstrap; few clusters; multi-way clusters. (search for similar items in EconPapers)
JEL-codes: C12 C21 C23 (search for similar items in EconPapers)
Date: 2010-03-24
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http://repec.dss.ucdavis.edu/files/T29sWVnXspMuzCnsfhRU5HGV/10-6.pdf (application/pdf)

Related works:
Working Paper: Robust inference with clustered data (2011) Downloads
Working Paper: Robust Inference with Clustered Data (2010) Downloads
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