Copula Analysis of Correlated Counts

Esther Hee Lee

A chapter in Bayesian Model Comparison, 2014, vol. 34, pp 325-348 from Emerald Group Publishing Limited

Abstract: Copula modeling enables the analysis of multivariate count data that has previously required imposition of potentially undesirable correlation restrictions or has limited attention to models with only a few outcomes. This article presents a method for analyzing correlated counts that is appealing because it retains well-known marginal distributions for each response while simultaneously allowing for flexible correlations among the outcomes. The proposed framework extends the applicability of the method to settings with high-dimensional outcomes and provides an efficient simulation method to generate the correlation matrix in a single step. Another open problem that is tackled is that of model comparison. In particular, the article presents techniques for estimating marginal likelihoods and Bayes factors in copula models. The methodology is implemented in a study of the joint behavior of four categories of US technology patents. The results reveal that patent counts exhibit high levels of correlation among categories and that joint modeling is crucial for eliciting the interactions among these variables.

Keywords: Copula; count data; marginal likelihood; Markov chain Monte Carlo (MCMC); C11; C25; C52 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320140000034021

DOI: 10.1108/S0731-905320140000034021

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