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Unbiased estimation of the OLS covariance matrix when the errors are clustered

Author

Listed:
  • Tom Boot

    (University of Groningen)

  • Gianmaria Niccodemi

    (University of Groningen)

  • Tom Wansbeek

    (University of Groningen)

Abstract

When data are clustered, common practice has become to do OLS and use an estimator of the covariance matrix of the OLS estimator that comes close to unbiasedness. In this paper, we derive an estimator that is unbiased when the random-effects model holds. We do the same for two more general structures. We study the usefulness of these estimators against others by simulation, the size of the t-test being the criterion. Our findings suggest that the choice of estimator hardly matters when the regressor has the same distribution over the clusters. But when the regressor is a cluster-specific treatment variable, the choice does matter and the unbiased estimator we propose for the random-effects model shows excellent performance, even when the clusters are highly unbalanced.

Suggested Citation

  • Tom Boot & Gianmaria Niccodemi & Tom Wansbeek, 2023. "Unbiased estimation of the OLS covariance matrix when the errors are clustered," Empirical Economics, Springer, vol. 64(6), pages 2511-2533, June.
    • Handle: RePEc:spr:empeco:v:64:y:2023:i:6:d:10.1007_s00181-023-02379-w
    DOI: 10.1007/s00181-023-02379-w
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    References listed on IDEAS

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    Cited by:

    1. James G. MacKinnon & Morten {O}rregaard Nielsen & Matthew D. Webb, 2024. "Jackknife inference with two-way clustering," Papers 2406.08880, arXiv.org.

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