CONSISTENT ESTIMATION OF ZERO‐INFLATED COUNT MODELS

TLDR

Applications of zero‑inflated count data models have proliferated in health economics, yet maximum likelihood estimators are not robust to misspecification. This article proposes Poisson quasi‑likelihood estimators as an alternative. The authors illustrate the advantages of the Poisson quasi‑likelihood approach through Monte Carlo simulations and an application to health‑service demand. These estimators are consistent in the presence of excess zeros without specifying the full distribution, as shown by simulations and the health‑service demand application. © 2012 John Wiley & Sons, Ltd.

Abstract

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. Copyright © 2012 John Wiley & Sons, Ltd.