Homogeneity and Sparsity Analysis for High-Dimensional Panel Data Models

• Published in: Journal of business & economic statistics Vol. 42; no. 1; pp. 26 - 35
• Main Authors: Wang, Wu, Zhu, Zhongyi
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 2024; Taylor & Francis Ltd
• Subjects: ADMM algorithm; Group detection; Heterogeneity; Panel data; Penalized regression
• ISSN: 0735-0015; 1537-2707
• DOI: 10.1080/07350015.2022.2140667

In this article, we are interested in detecting latent group structures and significant covariates in a high-dimensional panel data model with both individual and time fixed effects. The slope coefficients of the model are assumed to be subject dependent, and there exist group structures where the slope coefficients are homogeneous within groups and heterogeneous between groups. We develop a penalized estimator for recovering the group structures and the sparsity patterns simultaneously. We propose a new algorithm to optimize the objective function. Furthermore, we propose a strategy to reduce the computational complexity by pruning the penalty terms in the objective function, which also improves the accuracy of group structure detection. The proposed estimator can recover the latent group structures and the sparsity patterns consistently in large samples. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies and illustrated with a real dataset.