Title: Generalized Method of Moments with Heterogeneous Validity of Moment Conditions in Panel Data Models

Speaker: Zhan Gao, University of Southern California

Time：2024/12/16, 10:00-11:30

Venue:106 Zhonghui Building

About the speaker

Zhan Gao is a Ph.D. Candidate in Economics at University of Southern California. He obtained B.Sc. in Mathematics and M.Phil. in Economics from The Chinese University of Hong Kong. His research concentrates on econometrics, machine learning and labor economics. He has published papers in leading academic journals including Journal of Econometrics, Empirical Economics and Statistical Methods in Medical Research, and served as an referee for Journal of Business & Economic Statistics and Econometric Reviews.

Abstract

This paper provides a unified framework for the selection of valid moment conditions and detection of latent group structures based on the moment condition validity in general nonlinear generalized method of moments (GMM) panel data models. It accommodates a diverging number of moment conditions and group-specific heterogeneous validity of moment conditions across agents. The proposed method integrates the pairwise adaptive fused Lasso and the adaptive Lasso regularization into the GMM estimation. The estimator is shown to be consistent and simultaneously achieves classification and moment selection consistency. The asymptotic distribution of a post-regularization estimator is derived, and its oracle properties are established. The finite-sample performance of the proposed method is evaluated through a Monte Carlo simulation experiment. The method is applied to empirically investigate the impact of agricultural productivity shocks on rural-to-urban migration in China.