Distribution-Free Estimation of Zero-Inflated Models with Unobserved Heterogeneity

Count models often best describe the nature of data in health economics, but the presence of fixed effects with excess zeros and overdispersion strictly limits the choice of estimation methods. This paper presents a quasi-conditional likelihood method to consistently estimate models with excess zeros and unobserved individual heterogeneity when the true generating process is unknown. Monte Carlo simulation studies show that our zero-inflated quasi-conditional maximum likelihood (ZI-QCML) estimator outperforms other methods and is robust to distributional misspecifications. We apply the ZI-QCML estimator to analyze the frequency of doctor visits.