Estimation of Latent Group Structures in Time-Varying Panel Data Models

Paul Haimerl; Stephan Smeekes; Ines Wilms

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arXiv · 2503.23165

Estimation of Latent Group Structures in Time-Varying Panel Data Models

Paul Haimerl·Stephan Smeekes·Ines Wilms

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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.

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BibTeX RIS

Paul Haimerl, Stephan Smeekes, Ines Wilms. 2025-03-29. Estimation of Latent Group Structures in Time-Varying Panel Data Models. https://arxiv.org/abs/2503.23165

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