title: Handling endogeneity and nonnegativity in correlated random effects models: Evidence from ambulatory expenditure.
abstract: We describe a mixed-effects model for nonnegative continuous cross-sectional data in a two-part modelling framework. A potentially endogenous binary variable is included in the model specification and association between the outcomes is modeled through a (discrete) latent structure. We show how model parameters can be estimated in a finite mixture context, allowing for skewness, multivariate association between random effects and endogeneity. The model behavior is investigated through a large-scale simulation experiment. The proposed model is computationally parsimonious and seems to produce acceptable results even if the underlying random effects structure follows a continuous parametric (e.g. Gaussian) distribution. The proposed approach is motivated by the analysis of a sample taken from the Medical Expenditure Panel Survey. The analyzed outcome, that is ambulatory health expenditure, is a mixture of zeros and continuous values. The effects of socio-demographic characteristics on health expenditure are investigated and, as a by-product of the estimation procedure, two subpopulations (i.e. high and low users) are identified.
authors.affiliation: Dipartimento di Scienze Politiche, Università di Roma Tre, Roma, Italy.|Southampton Statistical Sciences Research Institute and School of Mathematics, University of Southampton, Southampton, UK.
authors.forename: Antonello
authors.initials: A
authors.lastname: Maruotti
authors.affiliation: Dipartimento di Scienze Statistiche, Sapienza Università di Roma, Italy.|Imperial College Business School, Imperial College, London, UK.
authors.forename: Valentina
authors.initials: V
authors.lastname: Raponi
authors.affiliation: Dipartimento di Scienze Politiche, Università di Roma Tre, Roma, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Lagona
mesh_terms: D000553:Ambulatory Care / Q000191:economics*; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D013269:Stochastic Processes
keywords: Correlated random effects models; Endogenous selectivity; Health care expenditure; Multivariate mixed-type data; Two-part model