Estimation of Latent Group Structures in Time-Varying Panel Data Models
Abstract
We consider panel data models where coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. To jointly estimate the group membership and group-specific coefficient trajectories, we propose FUSE-TIME, a pairwise adaptive group fused-Lasso estimator combined with polynomial spline sieves. We establish consistency, derive the asymptotic distributions of the penalized sieve estimator and its post-selection version, and show oracle efficiency. Monte Carlo experiments demonstrate strong finite-sample performance in terms of estimation accuracy and group identification. An application to the CO2 intensity of GDP highlights the relevance of addressing both cross-sectional heterogeneity and time-variance in empirical exercises.
Keywords
Cite
@article{arxiv.2503.23165,
title = {Estimation of Latent Group Structures in Time-Varying Panel Data Models},
author = {Paul Haimerl and Stephan Smeekes and Ines Wilms},
journal= {arXiv preprint arXiv:2503.23165},
year = {2025}
}