Stabilized Latent Group Structures Identification for Panel Data: A Regularization-Free Approach
Prof. Xingbai XU
Tenured Full Professor
Wang Yanan Institute for Studies in Economics &
The School of Economics
Xiamen University
Date: 26 February 2026 (Thursday)
Time: 14:30-16:00
Venue: E22-G008
Host: Prof. Yi DING, Assistant Professor in Business Economics
Abstract
One of the major challenges in identifying latent group structures in panel data is to determine the number of groups. Most existing approaches rely on minimizing an information criterion. However, these methods are often sensitive to the choice of tuning parameters, rendering the selection process ad hoc and unstable in finite samples. This paper develops a fully data-driven selection procedure that circumvents the need for regularization by minimizing a clustering instability score. We propose a valid resampling scheme that partitions data along the cross-sectional dimension. We show that the proposed criterion achieves selection consistency under mild conditions, with the error probability decaying exponentially in the number of individuals. Unlike model-specific information criteria, our framework is versatile and can be seamlessly integrated with various detection algorithms (e.g., K-means, SBSA) across both linear and nonlinear models. Numerical experiments confirm its superior robustness and accuracy compared to conventional methods. We apply our algorithm to study the relationship between economic development and carbon dioxide emissions. The proposed methodology is implemented in the accompanying R package StableGroup. This is based on joint work with Tuo Liu, Chuang Wan and Wuyi Wang.
Speaker
Prof. Xingbai XU is a tenured professor at Xiamen University. He is also a doctoral supervisor and a Nanqiang Outstanding Young Talent (Type A) at the university. He obtained his doctoral degree in Economics from The Ohio State University in 2016. His research focuses on spatial econometrics, network econometrics, Bayesian econometrics, and panel data analysis. His work has been published in academic journals such as the Journal of Econometrics, Econometric Theory, and Journal of Business and Economic Statistics. He has led a number of Natural Science Foundation of China research projects.
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