Asymptotic Theory for Clustered Samples

with Bruce E. Hansen (University of Wisconsin-Madison), Journal of Econometrics (2019) 210(2), 268-290

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.


    • pdf, arXiv

    • Winner of the 2020 Zellner Award for the best paper in theoretical econometrics published by the JoE in 2018 and 2019