We describe a mixed-effects model for non-negative 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, i.e. 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.

Lagona, F., Maruotti, A., Raponi, V. (2016). Handling endogeneity and non-negativity in correlated random effects models: evidence from ambulatory expenditure. BIOMETRICAL JOURNAL, 58(2), 280-302 [10.1002/bimj.201400121].

Handling endogeneity and non-negativity in correlated random effects models: evidence from ambulatory expenditure

LAGONA, Francesco;
2016

Abstract

We describe a mixed-effects model for non-negative 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, i.e. 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.
Lagona, F., Maruotti, A., Raponi, V. (2016). Handling endogeneity and non-negativity in correlated random effects models: evidence from ambulatory expenditure. BIOMETRICAL JOURNAL, 58(2), 280-302 [10.1002/bimj.201400121].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/115296
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