Estimation of Latent Group Structures in Time-Varying Panel Data Models
This paper addresses the dual heterogeneity in panel data—cross-sectional latent group structures (homogeneous within groups, heterogeneous across groups) and smoothly evolving time-varying coefficients. We propose a time-varying latent group panel model that jointly captures both features. Methodologically, we innovatively integrate adaptive pairwise grouping fusion Lasso—enabling automatic group identification—with polynomial or B-spline bases to flexibly model coefficient trajectories over time, thereby unifying latent grouping and smooth temporal variation for the first time. Theoretically, we establish asymptotic normality and oracle efficiency for both the penalized and post-selection estimators. Simulation studies demonstrate high grouping accuracy and low estimation bias. Empirical application to global GDP carbon intensity reveals significant cross-country latent grouping and time-varying convergence patterns, confirming the method’s statistical robustness and substantive interpretability.