A simple bivariate count data regression model

Shiferaw Gurmu () and John Elder ()
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Shiferaw Gurmu: Georgia State University

Economics Bulletin, 2007, vol. 3, issue 11, pages 1-10

Abstract: This paper develops a simple bivariate count data regression model in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components. Unlike existing commonly used bivariate models, we obtain a computationally simple closed form of the model with an unrestricted correlation pattern. An application to Medicaid utilization is provided.

Keywords: Bivariate counts; discrete model; Medicaid.; series estimation; truncation; unobserved heterogeneity; unrestricted correlation (search for similar items in EconPapers)
JEL-codes: C3 C4 (search for similar items in EconPapers)
Date: 2007-04-01
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