title: Distribution-free estimation of zero-inflated models with unobserved heterogeneity.
abstract: This paper presents a quasi-conditional likelihood method for the consistent estimation of both continuous and count data models with excess zeros and unobserved individual heterogeneity when the true data 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.
authors.affiliation: 1 Department of Economics, University of Washington, USA.
authors.forename: Rodica
authors.initials: R
authors.lastname: Gilles
authors.affiliation: 2 Department of Economics, Korea University, Korea.
authors.forename: Seik
authors.initials: S
authors.lastname: Kim
mesh_terms: D005260:Female; D006801:Humans; D016013:Likelihood Functions; D009010:Monte Carlo Method; D009819:Office Visits / Q000706:statistics & numerical data*
keywords: Excess zeros; nonnegative data; quasi-likelihood estimation; robust estimation; zero inflation