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Asymptotic theory for clustered samples

Bruce E. Hansen and Seojeong Lee ()

Journal of Econometrics, 2019, vol. 210, issue 2, 268-290

Abstract: We provide a complete asymptotic distribution theory for clustered data with a large number of independent groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous and unbounded cluster sizes. Our conditions cleanly nest the classical results for i.n.i.d. observations, in the sense that our conditions specialize to the classical conditions under independent sampling. We use this theory to develop a full asymptotic distribution theory for estimation based on linear least-squares, 2SLS, nonlinear MLE, and nonlinear GMM.

Date: 2019
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Related works:
Working Paper: Asymptotic Theory for Clustered Samples (2019) Downloads
Working Paper: Asymptotic Theory for Clustered Samples (2017) Downloads
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