Inference for clustered data

• Autores: Chang Hyung Lee, Douglas G. Steigerwald
• Localización: The Stata journal, ISSN 1536-867X, Vol. 18, Nº. 2, 2018, págs. 447-460
• Idioma: inglés
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• Resumen

  • 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.