PT  - JOURNAL ARTICLE
AU  - Colin Cameron, A.
AU  - Miller, Douglas L.
TI  - A Practitioner’s Guide to Cluster-Robust Inference
AID  - 10.3368/jhr.50.2.317
DP  - 2015 Mar 31
TA  - Journal of Human Resources
PG  - 317--372
VI  - 50
IP  - 2
4099  - http://jhr.uwpress.org/content/50/2/317.short
4100  - http://jhr.uwpress.org/content/50/2/317.full
SO  - J Hum Resour2015 Mar 31; 50
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.