Title: Asymptotic Properties of M-estimators with Finite Populations under Cluster Sampling and Cluster Assignment

About the speaker:

Ruonan Xu is an Assistant Professor specializing in Econometrics. She joined the Department of Economics at Rutgers University in Fall 2020. Her current research focuses on finite population inference with cross-sectional dependence. She received her Ph.D. in Economics from Michigan State University in 2020 and B.A. in Mathematical Economics from Fudan University in 2015.

Abstract:

I establish asymptotic properties of M-estimators under finite populations with clustered data, allowing for unbalanced and unbounded cluster sizes in the limit. I distinguish between two situations that justify computing clustered standard errors: i) cluster sampling induced by random sampling of groups of units, and ii) cluster assignment caused by the correlated assignment of “treatment” within groups. The finite population cluster-robust asymptotic variance is found to be no “larger” than its infinite population counterpart. I also show that one should only use clustered standard errors when there is cluster sampling and (or) cluster assignment for a generalclass of estimators.