SEMIPARAMETRIC ESTIMATION OF HETEROGENEOUS COUNT DATA MODELS

Unobserved heterogeneity in a stochastic model is usually represented by a mixing distribution. In this paper a semiparametric estimator is adapted to over-dispersed Poisson regression models. No assumptions ar e needed about the estimated mixing distribution. The parameters of in cluded explanatory variables are estimated at the same time. The appli cability and promising properties of the method are illustrated. Empir ically the estimator is applied to a coffee purchase model and to a bu siness travel frequency model subject to zero truncation. The approach is useful, e.g., in marketing research where socio-demographic variab les as well as marketing instruments can be included as explanatory va riables.