Quotation Hirk, Rainer, Hornik, Kurt, Vana, Laura. 2018. Multivariate ordinal regression models: an analysis of corporate credit ratings. Statistical Methods and Applications. 28 (3), 507-539.




Correlated ordinal data typically arises from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal regression models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. Using simulated data sets with varying number of subjects, we investigate the performance of the pairwise likelihood estimates and find them to be robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor’s, Moody’s and Fitch). Firm-level and stock price data for publicly traded US firms as well as an unbalanced panel of issuer credit ratings are collected and analyzed to illustrate the proposed framewor


Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Statistical Methods and Applications
Citation Index SCI
WU-Journal-Rating new VW-D
Language English
Title Multivariate ordinal regression models: an analysis of corporate credit ratings
Volume 28
Number 3
Year 2018
Page from 507
Page to 539
Reviewed? Y
URL http://link.springer.com/article/10.1007/s10260-018-00437-7
DOI https://dx.doi.org/10.1007/s10260-018-00437-7
Open Access Y
Open Access Link https://link.springer.com/article/10.1007%2Fs10260-018-00437-7


Hirk, Rainer (Details)
Hornik, Kurt (Details)
Vana Gür, Laura (Details)
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
5361 Financial management (Details)
5701 Applied statistics (Details)
Google Scholar: Search