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Title: Composite likelihood estimation of an autoregressive panel ordered probit model with random effects Authors:  Kerem Tuzcuoglu - Bank of Canada (Canada) [presenting]
Abstract: Modeling and estimating autocorrelated discrete data can be challenging. We use an autoregressive panel ordered probit model where the autocorrelation in the latent variable drives the serial correlation in the discrete variable. In such a non-linear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve a much lower order of integration. However, parameter identification becomes problematic since the information employed in lower-dimensional distributions may not be rich enough for identification. Therefore, we characterize types of composite likelihoods that are valid for this model and study conditions under which the parameters can be identified. Moreover, we provide consistency and asymptotic normality results of the pairwise composite likelihood estimator and conduct Monte Carlo studies to assess its finite-sample performances. Finally, we apply our method to analyze credit ratings. The results indicate a significant improvement in the estimated probabilities for rating transitions compared with static models.