Estimating the demand for health care with panel data: a semiparametric Bayesian approach | Zendy

Jochmann Markus | Zendy; LeónGonzález Roberto | Zendy

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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.

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