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Robust Inference with Clustered Data

  • A. Colin Cameron
  • Douglas L. Miller

    (Department of Economics, University of California Davis)

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.

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File URL: http://wp.econ.ucdavis.edu/10-6.pdf
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Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 106.

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Length: 28
Date of creation: 25 Mar 2010
Date of revision:
Handle: RePEc:cda:wpaper:10-6
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  1. Lara Shore-Sheppard, 1996. "The Precision of Instrumental Variables Estimates With Grouped Data," Working Papers 753, Princeton University, Department of Economics, Industrial Relations Section..
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  3. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-in-Differences Estimates?," The Quarterly Journal of Economics, MIT Press, vol. 119(1), pages 249-275, February.
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  19. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
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  26. Angrist, Joshua & Lavy, Victor, 2002. "The Effect of High School Matriculation Awards: Evidence from Randomized Trials," CEPR Discussion Papers 3827, C.E.P.R. Discussion Papers.
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