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Research ArticleSymposium on Empirical Methods

A Practitioner’s Guide to Cluster-Robust Inference

A. Colin Cameron and Douglas L. Miller
Journal of Human Resources, March 2015, 50 (2) 317-372; DOI: https://doi.org/10.3368/jhr.50.2.317
A. Colin Cameron
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Douglas L. Miller
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Abstract

We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. In such settings, default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster-specific fixed effects, few clusters, multiway clustering, and estimators other than OLS.

  • Received March 2013.
  • Accepted January 2014.
  • © 2015 by the Board of Regents of the University of Wisconsin System

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Journal of Human Resources: 50 (2)
Journal of Human Resources
Vol. 50, Issue 2
31 Mar 2015
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A Practitioner’s Guide to Cluster-Robust Inference
A. Colin Cameron, Douglas L. Miller
Journal of Human Resources Mar 2015, 50 (2) 317-372; DOI: 10.3368/jhr.50.2.317

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A Practitioner’s Guide to Cluster-Robust Inference
A. Colin Cameron, Douglas L. Miller
Journal of Human Resources Mar 2015, 50 (2) 317-372; DOI: 10.3368/jhr.50.2.317
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