Grouped fixed effects regularization for binary choice models
Claudia Pigini,
Alessandro Pionati and
Francesco Valentini
Papers from arXiv.org
Abstract:
We study the application of the Grouped Fixed Effects (GFE) estimator (Bonhomme et al., ECMTA 90(2):625-643, 2022) to binary choice models for network and panel data. This approach discretizes unobserved heterogeneity via k-means clustering and performs maximum likelihood estimation, reducing the number of fixed effects in finite samples. This regularization helps analyze small/sparse networks and rare events by mitigating complete separation, which can lead to data loss. We focus on dynamic models with few state transitions and network formation models for sparse networks. The effectiveness of this method is demonstrated through simulations and real data applications.
Date: 2025-02
New Economics Papers: this item is included in nep-dcm and nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://arxiv.org/pdf/2502.06446 Latest version (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2502.06446
Access Statistics for this paper
More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().