A penalized U-MIDAS multinomial logit model with applications to corporate credit ratings

Cuixia Jiang, Junwei Sun and Qifa Xu

The North American Journal of Economics and Finance, 2025, vol. 76, issue C

Abstract: We develop a penalized U-MIDAS-Mlogit model by introducing the group LASSO penalty into the unrestricted MIDAS multinomial logit model. This penalized U-MIDAS-Mlogit model can implement multinomial classification in a high-dimensional mixed-frequency data environment. We apply it to credit ratings for listed companies in China over the period 2008–2023. The penalized U-MIDAS-Mlogit model can extract pivotal information from high-frequency financial variables and low-frequency internal and external governance indicators. It outperforms several competing models in predicting credit ratings.

Keywords: Corporate credit ratings; U-MIDAS regression; Multinomial logit model; Group LASSO (search for similar items in EconPapers)
JEL-codes: C58 G32 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S106294082500021X
Full text for ScienceDirect subscribers only

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:eee:ecofin:v:76:y:2025:i:c:s106294082500021x

DOI: 10.1016/j.najef.2025.102381

Access Statistics for this article

The North American Journal of Economics and Finance is currently edited by Hamid Beladi

More articles in The North American Journal of Economics and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().