 Multivariate ordinal regression for multiple repeated measurementsLaura Vana-Gür Computational Statistics & Data Analysis, 2024, vol. 199, issue C Abstract: A multivariate ordinal regression model which allows the joint modeling of three-dimensional panel data containing both repeated and multiple measurements for a collection of subjects is proposed. This is achieved by a multivariate autoregressive structure on the errors of the latent variables underlying the ordinal responses, which accounts for the correlations at a single point in time and the persistence over time. The error distribution is assumed to be normal or Student-t distributed. The estimation is performed using composite likelihood methods. Through several simulation exercises, the quality of the estimates in different settings as well as in comparison with a Bayesian approach is investigated. The simulation study confirms that the estimation procedure is able to recover the model parameters well and is competitive in terms of computation time. Finally, the framework is illustrated using a data set containing bankruptcy and credit rating information for US exchange-listed companies. Keywords: Composite likelihood; Multivariate autoregressive error; Multivariate ordinal regression model; Panel data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:199:y:2024:i:c:s0167947324000975 DOI: 10.1016/j.csda.2024.108013 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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