Inference for clustered data

Chang Hyung Lee () and Douglas Steigerwald
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Chang Hyung Lee: University of California, Santa Barbara

Stata Journal, 2018, vol. 18, issue 2, 447-460

Abstract: In this article, we introduce clusteff, a community-contributed com- mand 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 clus- ters. We provide a decision tree for cluster–robust analysis, demonstrate the use of clusteff, and recommend methods to minimize the size distortion.

Keywords: clusteff; cluster heterogeneity (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (14)

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