In this paper we propose 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. This is achieved by a multivariate autoregressive structure on the errors of the latent variables underlying the ordinal responses, where we distinguish between 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. We perform several simulation exercises to investigate the quality of the estimates in different settings as well as in comparison with a Bayesian approach. The simulation study confirms that the estimation procedure is able to recover the model parameters well and is competitive in terms of computation time. We also introduce R package mvordflex and illustrate how this implementation can be used to estimate the proposed model in a user-friendly, convenient way. Finally, we illustrate the framework on a data set containing firm failure and credit ratings information from the rating agencies S&P and Moody's for US listed companies.

翻译：本文提出一种多元有序回归模型，可对包含重复测量与多重测量的三维面板数据进行联合建模。该模型通过在序数响应变量潜在误差项上构建多元自回归结构，区分同一时间点的相关性以及时间维度上的持续性。误差分布假定为正态分布或学生t分布，并采用复合似然方法进行参数估计。我们通过多项模拟实验评估不同场景下的估计质量，并与贝叶斯方法进行对比。模拟研究证实该估计方法能够有效恢复模型参数，且在计算效率上具有竞争力。此外，我们开发了R包mvordflex，以用户友好的便捷方式展示该模型的实现应用。最后，通过包含标普与穆迪评级机构对美国上市公司财务困境与信用评级的数据集，验证该框架的实际效果。