Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/197930
Authors: 
Tuzcuoglu, Kerem
Year of Publication: 
2019
Series/Report no.: 
Bank of Canada Staff Working Paper 2019-16
Abstract: 
Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve 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 simulations to assess its finite-sample performances. Finally, we apply our method to analyze credit ratings. The results indicate a significant improvement in the estimated transition probabilities between rating classes compared with static models.
Subjects: 
Econometric and statistical methods
Economic models
Credit risk management
JEL: 
C23
C25
C58
G24
Document Type: 
Working Paper
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