2015
DOI: 10.3368/jhr.50.2.317
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A Practitioner’s Guide to Cluster-Robust Inference

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 aft… Show more

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Cited by 3,545 publications
(2,332 citation statements)
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References 85 publications
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“…If there are a small number of clusters, Cameron et al (2008) and Cameron and Miller (2015) propose using the wild bootstrap to obtain correctly sized tests and confidence intervals. The method is, however, not so trivial to implement and, as acknowledged by the authors, computationally intensive for forming confidence intervals.…”
Section: Inference With a Small Number Of Clustersmentioning
confidence: 99%
“…If there are a small number of clusters, Cameron et al (2008) and Cameron and Miller (2015) propose using the wild bootstrap to obtain correctly sized tests and confidence intervals. The method is, however, not so trivial to implement and, as acknowledged by the authors, computationally intensive for forming confidence intervals.…”
Section: Inference With a Small Number Of Clustersmentioning
confidence: 99%
“…Previous research has shown that these preferences are determined to a large extent by mothers' level of education and their immigrant background [22,28,29]. Equation (1) was specified as a linear probability model, where the standard errors were clustered on hospital [30]. Support for defensive medicine would imply that > 0.…”
Section: Main Analysis -Empirical Specificationmentioning
confidence: 99%
“…We expect this coefficient to be positive. The standard errors were clustered at the hospital level [30]. We also present results without hospital fixed effects included in Model (3).…”
Section: A Longer Time Seriesmentioning
confidence: 99%
“…To adjust for potential bias in hypothesis tests due to the small number of clusters, I use the t-distribution with degrees of freedom equal to the number of clusters minus the number of regressors rather than using the standard normal reference distribution (Donald and Lang, 2007;Cameron and Miller, forthcoming). An alternative approach to session-level clustering also suggested by Fréchette (2012) is controlling for feedback effects but assuming independence between subjects conditional on this feedback.…”
Section: Contribution Ratesmentioning
confidence: 99%