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

• Tác giả: Paul Haimerl, Stephan Smeekes, Ines Wilms
• Ngôn ngữ: eng
• Ký hiệu phân loại: 001.434 Experimental method
• Thông tin xuất bản: 2025
• Bộ sưu tập: Metadata
• ID: 226833

Tóm tắt

We introduce a panel data model where coefficients vary both over time and the cross-section. Slope coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. The group structure is identified using a pairwise adaptive group fused-Lasso penalty. The trajectories of time-varying coefficients are estimated via polynomial spline functions. We derive the asymptotic distributions of the penalized and post-selection estimators and show their oracle efficiency. A simulation study demonstrates excellent finite sample properties. An application to the emission intensity of GDP highlights the relevance of addressing cross-sectional heterogeneity and time-variance in empirical settings.