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

Tom Boot, Gianmaria Niccodemi and Tom Wansbeek ()
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Tom Boot: University of Groningen
Gianmaria Niccodemi: University of Groningen
Tom Wansbeek: University of Groningen

A chapter in Advances in Applied Econometrics, 2024, pp 47-69 from Springer

Abstract: 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.

Keywords: Clustered errors; Degrees-of-freedom correction; Placebo regression; Treatment effect; Unbiased estimator (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:adschp:978-3-031-48385-1_3

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DOI: 10.1007/978-3-031-48385-1_3

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