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000003980 02470 $$ahttps://doi.org/10.6084/m9.figshare.19232834
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000003980 245__ $$a(Data and code for) Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects
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000003980 269__ $$a2022-03-31
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000003980 506__ $$aFiles may be downloaded from digital repository linked in the DOI field
000003980 520__ $$aModeling and estimating autocorrelated discrete data can be challenging. In this article, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve a much lower order of integration. However, parameter identification might potentially become problematic since the information employed in lower dimensional distributions may not be rich enough for identification. Therefore, we characterize types of composite likelihoods that are valid for this model and study conditions under which the parameters can be identified. Moreover, we provide consistency and asymptotic normality results for two different composite likelihood estimators and conduct Monte Carlo studies to assess their finite-sample performances. Finally, we apply our method to analyze corporate bond ratings. Supplementary materials for this article are available online. <p> <p> Supplemental material for peer-reviewed article published in Journal of Economic & Business Statistics. Paper published online March 31, 2022.$$7Abstract
000003980 540__ $$aCreative Commons Attribution 4.0 International$$uhttps://creativecommons.org/licenses/by/4.0/legalcode$$fCC-BY-4.0
000003980 7001_ $$aTuzcuoglu, Kerem$$7Personal$$uBank of Canada$$3https://ror.org/05cc98565$$5ROR
000003980 791__ $$aJournalArticle$$eIsSupplementTo$$iReproducibility package is associated with peer-reviewed article$$w10.1080/07350015.2022.2044829$$c2022$$dJournal of Business & Economic Statistics (Taylor & Francis)$$tComposite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects$$2DOI$$j41$$k2$$q593$$o607
000003980 791__ $$aText$$eIsSupplementTo$$iAssociated article is first published as Bank of Canada Staff Working Paper$$w10.34989/swp-2019-16$$c2019$$dBank of Canada$$tComposite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects$$2DOI$$jStaff Working Paper$$k2019-16
000003980 791__ $$w10.6084/m9.figshare.19232834.v1$$eIsNewVersionOf$$2DOI$$tComposite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects$$b1$$aDataset$$iDataset is update to an earlier version$$dFigshare$$c2022-02-24
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000003980 909CO $$ooai:www.oar-rao.bank-banque-canada.ca:3980$$pbibliographic
000003980 937__ $$aTuzcuoglu, K$$b2022$$cComposite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects$$dJournal of Business & Economic Statistics$$e41$$f2$$ghttps://doi.org/10.1080/07350015.2022.2044829
000003980 980__ $$aStaff Research
000003980 980__ $$aRDM
000003980 980__ $$aResearch Reproducibility Packages
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