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

Tom Boot, Gianmaria Niccodemi, Tom Wansbeek

arXiv 20 Jun 2022 · Econometrics · publishedEmpirical Economics (2023)

arXiv:2206.09644 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

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appendix boundary found by appendix_titled_section at “Appendix A: Derivation of the unbiased variance estimators” · 65% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Liang, K.-Y. and Zeger, S.L (1986) Longitudinal data analysis using generalized linear models0.51121100%
2Bell, R.M. and McCaffrey, D.F (2002) Bias reduction in standard errors for linear regression with multi-stage samples0.40511100%
3Breusch, T.S. and A.R. Pagan (1980) The Lagrange multiplier test and its applications to model specification in econometrics0.40511100%
4Cameron, A.C. and Miller, D.L (2015) A practitioner`s guide to cluster-robust inference0.40511100%
5Cameron, A.C. and P.K. Trivedi (2005) Microeconometrics0.40511100%
6Donald, S.G. and Lang, K (2007) Inference with difference-in-differences and other panel data0.40511100%
7Hansen, B.E. and Lee, S (2019) Asymptotic theory for clustered samples0.40511100%
8Hartley, H. and Rao, J. and Kiefer, G (1969) Variance estimation with one unit per stratum0.40511100%
9Ibragimov, R. and Müller, U.K (2016) Inference with few heterogeneous clusters0.40511100%
10Kline, P. and Saggio, R. and Sølvsten, M (2020) Leave-out estimation of variance components0.40511100%

Showing the top 10 of 15 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Inference on LATEs with covariates0.40511
2When Can We Trust Cluster-Robust Inference?0.40511