PAGFL: Joint Estimation and Identification of Latent Groups in Panel
Data Models
In panel data analysis, unobservable group structures are a common challenge. Disregarding group-level heterogeneity by assuming an entirely homogeneous panel can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty.
This package addresses this issue by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) <doi:10.1016/j.jeconom.2022.12.002>.
PAGFL is an efficient methodology to identify latent group structures and estimate group-specific coefficients simultaneously.
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[aut, cre],
Ali Mehrabani