Inference for clustered data
Chang Hyung Lee
Department of Economics
University of California, Santa Barbara
Santa Barbara, CA
[email protected]
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Douglas G. Steigerwald
Department of Economics
University of California, Santa Barbara
Santa Barbara, CA
[email protected]
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Abstract. In this article, we introduce clusteff, a community-contributed command
for checking the severity of cluster heterogeneity in cluster–robust analyses.
Cluster heterogeneity can cause a size distortion leading to underrejection of the
null hypothesis. Carter, Schnepel, and Steigerwald (2017, Review of Economics
and Statistics 99: 698–709) develop the effective number of clusters to reflect a
reduction in the degrees of freedom, thereby mirroring the distortion caused by
assuming homogeneous clusters. clusteff generates the effective number of clusters.
We provide a decision tree for cluster–robust analysis, demonstrate the use
of clusteff, and recommend methods to minimize the size distortion.
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Chang Hyung Lee, Douglas G. Steigerwald
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clusteff, cluster heterogeneity
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