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Cluster Sample Inference with Very Few Groups

Author

Listed:
  • Johan Vikström

    (IFAU Uppsala, Box 513, S-752 20 Uppsala, Sweden and UCLS, Department of Economics University, johan.vikstrom@ifau.uu.se)

Abstract

If the number of clusters equals the number of cluster-invariant regressors the usual clusters-specific standard errors cannot be computed. One important example is the difference in differences setting with two groups and two time periods. This paper introduces a new method to calculate how large the within cluster correlation of the model errors can be yet still enable OLS coefficients to retain statistical significance at a desired level. This sensitivity approach is applied to previous results regarding the effects of workers compensation benefits.

Suggested Citation

  • Johan Vikström, 2016. "Cluster Sample Inference with Very Few Groups," Econometrics Letters, Bilimsel Mektuplar Organizasyonu (Scientific letters), vol. 3(1), pages 1-10.
  • Handle: RePEc:bmo:bmoart:v:3:y:2016:i:1:p:1-10
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    File URL: http://dx.doi.org/10.5455/Elet.2016.3.1.1
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    More about this item

    Keywords

    Cluster effect; cluster robust; difference in difference; sensitivity; workers compensation.;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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