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Inference for clustered data

Author

Listed:
  • Chang Hyung Lee

    (University of California, Santa Barbara)

  • Douglas G. Steigerwald

    (University of California, Santa Barbara)

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.

Suggested Citation

  • Chang Hyung Lee & Douglas G. Steigerwald, 2018. "Inference for clustered data," Stata Journal, StataCorp LP, vol. 18(2), pages 447-460, June.
    • Handle: RePEc:tsj:stataj:v:18:y:2018:i:2:p:447-460
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    Citations

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    Cited by:

    1. Mikola, Derek & Webb, Matthew D., 2023. "Finish it and it is free: An evaluation of college graduation subsidies," Economics of Education Review, Elsevier, vol. 93(C).
    2. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2023. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust," Stata Journal, StataCorp LP, vol. 23(4), pages 942-982, December.
    3. Carpenter, Christopher S. & Gonzales, Gilbert & McKay, Tara & Sansone, Dario, 2020. "Effects of the Affordable Care Act Dependent Coverage Mandate on Health Insurance Coverage for Individuals in Same-Sex Couples," IZA Discussion Papers 13119, Institute of Labor Economics (IZA).
    4. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    5. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
    6. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    7. Gawain Heckley & Martin Nordin & Ulf‐G. Gerdtham, 2022. "The health returns of attending university for the marginally eligible student," Health Economics, John Wiley & Sons, Ltd., vol. 31(5), pages 877-903, May.
    8. Deb, Partha & Gangaram, Anjelica & Khajavi, Hoda Nouri, 2021. "The impact of the State Innovation Models Initiative on population health," Economics & Human Biology, Elsevier, vol. 42(C).
    9. Jeremy Edwards, 2021. "Can Institutional Transplants Work? A Reassessment of the Evidence from Nineteenth-Century Prussia," CESifo Working Paper Series 9333, CESifo.
    10. Chang Hyung Lee, 2020. "Minimum Wage Policy and Community College Enrollment Patterns," ILR Review, Cornell University, ILR School, vol. 73(1), pages 178-210, January.
    11. Child, Travers Barclay & Massoud, Nadia & Schabus, Mario & Zhou, Yifan, 2021. "Surprise election for Trump connections," Journal of Financial Economics, Elsevier, vol. 140(2), pages 676-697.

    More about this item

    Keywords

    clusteff; cluster heterogeneity;

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