Asymptotic Theory for Clustered Samples

Bruce Hansen () and Seojeong Lee

No 2017-18, Discussion Papers from School of Economics, The University of New South Wales

Abstract: We provide a complete asymptotic distribution theory for clustered data with a large number of 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.

Keywords: clustered data; law of large numbers; central limit theorem; clustered covariance matrix estimation (search for similar items in EconPapers)
JEL-codes: C12 C13 C31 C33 C36 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2017-12
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Asymptotic theory for clustered samples (2019)
Working Paper: Asymptotic Theory for Clustered Samples (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:swe:wpaper:2017-18

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