RT Journal Article
SR Electronic
T1 A Practitioner’s Guide to Cluster-Robust Inference
JF Journal of Human Resources
JO J Hum Resour
FD University of Wisconsin Press
SP 317
OP 372
DO 10.3368/jhr.50.2.317
VO 50
IS 2
A1 Colin Cameron, A.
A1 Miller, Douglas L.
YR 2015
UL http://jhr.uwpress.org/content/50/2/317.abstract
AB 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.