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A bivariate Poisson count data model using conditional probabilities
Author(s) -
Berkhout Peter,
Plug Erik
Publication year - 2004
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2004.00126.x
Subject(s) - bivariate analysis , mathematics , joint probability distribution , poisson distribution , count data , conditional probability distribution , bivariate data , statistics , marginal distribution , copula (linguistics) , poisson regression , correlation , econometrics , random variable , population , demography , geometry , sociology
The applied econometrics of bivariate count data predominantly focus on a bivariate Poisson density with a correlation structure that is very restrictive. The main limitation is that this bivariate distribution excludes zero and negative correlation. This paper introduces a new model which allows for a more flexible correlation structure. To this end the joint density is decomposed by means of the multiplication rule in marginal and conditional densities. Simulation experiments and an application of the model to recreational data are presented.