This paper proposes a computationally simple bivariate zero-inflated count data regression model with an unrestricted correlation pattern. An application to data with excess of zeros on the demand for health services is given.

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...

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 article develops a semiparametric estimation method for the bivariate count data regression model. We develop a series expansion approach in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components, and in which, unlike...

We study the factors affecting the employment probability of temporary assistance for needy families (TANF) recipients using recent quarterly panel data from Atlanta, Georgia. A central focus of our study is to determine whether the TANF recipient's proximity to job opportunity and the...

This article develops a modeling approach for a count data set for recreational boating trips that shows a frequency of zero counts significantly higher than that expected for Poisson distributed data. As alternatives to the Poisson regression, the authors consider several parametric and...

This paper motivates, exposits, and develops the variable augmentation specification test (VAST) approach from the perspective of generalized linear exponential family, which includes several parametric families widely used in applied econometrics and statistics. The approach is equivalent to...