  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.
