Flexible Bivariate Count Data Regression Models

• Activity Number: 519
• Type: Contributed
• Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Shiferaw Gurmu*+ and John Elder
• Companies: Georgia State University and Colorado State University
• Keywords: Series estimation ; Negative correlation ; Unobserved heterogeneity ; Multivariate counts ; Tobacco use ; Health care utilization

Abstract

The paper develops semiparametric estimation methods for bivariate count data regression models. We develop series expansion approach in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components, and in which, unlike existing commonly used models, positive as well as negative correlations are allowed. In implementation, we use bivariate expansions based on the generalized Laguerre polynomials. Extensions that accommodate for excess zeros, truncated and censored data and multivariate generalizations are also given. The first application examines the socio-economic and demographic determinants of tobacco use in the context of the joint modeling of the daily number of smoking tobacco and number of chewing tobacco based on household survey data. We also analyze jointly two health utilization measures, number of consultations with a doctor and non-doctor consultations. One of the key contributions is in obtaining a computationally tractable closed form of the model with flexible correlation structure. Results from the applications and simulation experiments confirm that the method is feasible and perform well.