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Estimating the demand for health care with panel data: a semiparametric Bayesian approach
Author(s) -
Jochmann Markus,
LeónGonzález Roberto
Publication year - 2004
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.936
Subject(s) - markov chain monte carlo , dirichlet process , econometrics , random effects model , bayesian probability , dirichlet distribution , panel data , computer science , variable order bayesian network , semiparametric model , statistics , bayesian inference , mathematics , nonparametric statistics , medicine , mathematical analysis , meta analysis , boundary value problem
This paper is concerned with the problem of estimating the demand for health care with panel data. A random effects model is specified within a semiparametric Bayesian approach using a Dirichlet process prior. This results in a very flexible distribution for both the random effects and the count variable. In particular, the model can be seen as a mixture distribution with a random number of components, and is therefore a natural extension of prevailing latent class models. A full Bayesian analysis using Markov chain Monte Carlo simulation methods is proposed. The methodology is illustrated with an application using data from Germany. Copyright © 2004 John Wiley & Sons, Ltd.