Copula, marginal distributions and model selection: a Bayesian note

Authors:
Ralph Dos Silva;Hedibert Freitas Lopes
Affiliations:
Australian School of Business, University of New South Wales, Sydney, Australia 2052;Graduate School of Business, University of Chicago, Chicago, USA 60637
Venue:
Statistics and Computing
Year:
2008

Citing 3
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Abstract

Copula functions and marginal distributions are combined to produce multivariate distributions. We show advantages of estimating all parameters of these models using the Bayesian approach, which can be done with standard Markov chain Monte Carlo algorithms. Deviance-based model selection criteria are also discussed when applied to copula models since they are invariant under monotone increasing transformations of the marginals. We focus on the deviance information criterion. The joint estimation takes into account all dependence structure of the parameters' posterior distributions in our chosen model selection criteria. Two Monte Carlo studies are conducted to show that model identification improves when the model parameters are jointly estimated. We study the Bayesian estimation of all unknown quantities at once considering bivariate copula functions and three known marginal distributions.