Estimation of Latent Group Structures in Time-Varying Panel Data Models
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911272470052864 |
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| author | Haimerl, Paul Smeekes, Stephan Wilms, Ines |
| author_facet | Haimerl, Paul Smeekes, Stephan Wilms, Ines |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimation of Latent Group Structures in Time-Varying Panel Data Models Haimerl, Paul Smeekes, Stephan Wilms, Ines Econometrics 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. |
| title | Estimation of Latent Group Structures in Time-Varying Panel Data Models |
| topic | Econometrics |
| url | https://arxiv.org/abs/2503.23165 |