A New Estimator for Standard Errors with Few Unbalanced Clusters

Gianmaria Niccodemi and Tom Wansbeek
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Gianmaria Niccodemi: Faculty of Humanities, Education and Social Sciences, University of Luxembourg, Esch-sur-Alzette, L-4366 Luxembourg, Luxembourg
Tom Wansbeek: Faculty of Economics and Business, University of Groningen, 9700 AV Groningen, The Netherlands

Econometrics, 2022, vol. 10, issue 1, 1-7

Abstract: In linear regression analysis, the estimator of the variance of the estimator of the regression coefficients should take into account the clustered nature of the data, if present, since using the standard textbook formula will in that case lead to a severe downward bias in the standard errors. This idea of a cluster-robust variance estimator (CRVE) generalizes to clusters the classical heteroskedasticity-robust estimator. Its justification is asymptotic in the number of clusters. Although an improvement, a considerable bias could remain when the number of clusters is low, the more so when regressors are correlated within cluster. In order to address these issues, two improved methods were proposed; one method, which we call CR2VE, was based on biased reduced linearization, while the other, CR3VE, can be seen as a jackknife estimator. The latter is unbiased under very strict conditions, in particular equal cluster size. To relax this condition, we introduce in this paper CR3VE- λ , a generalization of CR3VE where the cluster size is allowed to vary freely between clusters. We illustrate the performance of CR3VE- λ through simulations and we show that, especially when cluster sizes vary widely, it can outperform the other commonly used estimators.

Keywords: clustered data; few clusters; unbalanced clusters; cluster-robust variance estimator; inference (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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