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Mixed Marginal Copula Modeling

Journal Article


Abstract


  • This article extends the literature on copulas with discrete or continuous marginals to the case where some of the marginals are a mixture of discrete and continuous components. We do so by carefully defining the likelihood as the density of the observations with respect to a mixed measure. The treatment is quite general, although we focus on mixtures of Gaussian and Archimedean copulas. The inference is Bayesian with the estimation carried out by Markov chain Monte Carlo. We illustrate the methodology and algorithms by applying them to estimate a multivariate income dynamics model. Supplementary materials for this article are available online.

Publication Date


  • 2020

Citation


  • Gunawan, D., Khaled, M. A., & Kohn, R. (2020). Mixed Marginal Copula Modeling. Journal of Business and Economic Statistics, 38(1), 137-147. doi:10.1080/07350015.2018.1469998

Scopus Eid


  • 2-s2.0-85049803072

Start Page


  • 137

End Page


  • 147

Volume


  • 38

Issue


  • 1

Abstract


  • This article extends the literature on copulas with discrete or continuous marginals to the case where some of the marginals are a mixture of discrete and continuous components. We do so by carefully defining the likelihood as the density of the observations with respect to a mixed measure. The treatment is quite general, although we focus on mixtures of Gaussian and Archimedean copulas. The inference is Bayesian with the estimation carried out by Markov chain Monte Carlo. We illustrate the methodology and algorithms by applying them to estimate a multivariate income dynamics model. Supplementary materials for this article are available online.

Publication Date


  • 2020

Citation


  • Gunawan, D., Khaled, M. A., & Kohn, R. (2020). Mixed Marginal Copula Modeling. Journal of Business and Economic Statistics, 38(1), 137-147. doi:10.1080/07350015.2018.1469998

Scopus Eid


  • 2-s2.0-85049803072

Start Page


  • 137

End Page


  • 147

Volume


  • 38

Issue


  • 1