title: Consistent estimation of zero-inflated count models.
abstract: Applications of zero-inflated count data models have proliferated in health economics. However, zero-inflated Poisson or zero-inflated negative binomial maximum likelihood estimators are not robust to misspecification. This article proposes Poisson quasi-likelihood estimators as an alternative. These estimators are consistent in the presence of excess zeros without having to specify the full distribution. The advantages of the Poisson quasi-likelihood approach are illustrated in a series of Monte Carlo simulations and in an application to the demand for health services.
authors.affiliation: University of Zurich, Zürich, Switzerland.
authors.forename: Kevin E
authors.initials: KE
authors.lastname: Staub
authors.forename: Rainer
authors.initials: R
authors.lastname: Winkelmann
mesh_terms: D006301:Health Services Needs and Demand / Q000191:economics*; D006801:Humans; D016013:Likelihood Functions; D018803:Models, Economic; D015233:Models, Statistical; D009010:Monte Carlo Method; D016012:Poisson Distribution