Copulas have enjoyed increased usage in many areas of econometrics, including applications with discrete outcomes. However, Genest and Nešlehová (2007) present evidence that copulas for discrete outcomes are not identified, particularly when those discrete outcomes follow count distributions. This paper confirms the Genest and Nešlehová result using a series of simulation exercises. The paper then proceeds to show that those identification concerns diminish if the model has a regression structure such that the exogenous variable(s) generates additional variation in the outcomes and thus more completely covers the outcome domain
Bivariate Poisson models are appropriate for modelling paired count data. However, the bivariate Po...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
In competing risks models, the joint distribution of the event times is not identifiable even when t...
Multivariate discrete outcomes are common in a wide range of areas including insurance, finance, and...
Multivariate discrete outcomes are common in a wide range of areas including insurance, finance, and...
In this note, we elucidate some of the mathematical, statistical and epistemological issues involved...
The authors review various facts about copulas linking discrete distributions. They show how the pos...
Copulas have now become ubiquitous statistical tools for describing, analysing and modelling depende...
Bivariate Poisson models are appropriate for modelling paired count data. However, the bivariate Poi...
Bivariate Poisson models are appropriate for modeling paired count data. However the bivariate Pois...
Multivariate count data occur in several different disciplines. However, existing models do not offe...
In many cases of modeling bivariate count data, the interest lies on studying the association rather...
Abstract. In competing risks models, the joint distribution of the event times is not identifiable e...
In many cases of modeling bivariate count data, the interest lies on studying the association rather...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
Bivariate Poisson models are appropriate for modelling paired count data. However, the bivariate Po...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
In competing risks models, the joint distribution of the event times is not identifiable even when t...
Multivariate discrete outcomes are common in a wide range of areas including insurance, finance, and...
Multivariate discrete outcomes are common in a wide range of areas including insurance, finance, and...
In this note, we elucidate some of the mathematical, statistical and epistemological issues involved...
The authors review various facts about copulas linking discrete distributions. They show how the pos...
Copulas have now become ubiquitous statistical tools for describing, analysing and modelling depende...
Bivariate Poisson models are appropriate for modelling paired count data. However, the bivariate Poi...
Bivariate Poisson models are appropriate for modeling paired count data. However the bivariate Pois...
Multivariate count data occur in several different disciplines. However, existing models do not offe...
In many cases of modeling bivariate count data, the interest lies on studying the association rather...
Abstract. In competing risks models, the joint distribution of the event times is not identifiable e...
In many cases of modeling bivariate count data, the interest lies on studying the association rather...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
Bivariate Poisson models are appropriate for modelling paired count data. However, the bivariate Po...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
In competing risks models, the joint distribution of the event times is not identifiable even when t...