This paper is concerned with the problem of estimating the demand for health care with panel data. A random effects model is specifed in a semiparametric Bayesian fashion using a Dirichlet process prior. This results in a very exible mixture distribution with an in nite number of components for the random effects. Therefore, the model can be seen as a natural extension of prevailing latent class models. A full Bayesian analysis using Markov chain Monte Carlo (MCMC)simulation methods is discussed. The methodology is illustrated with an application using data from Germany.
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Paper provided by The University of Sheffield, Department of Economics in its series Working Papers with number
2003005.
Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data I10 - Health, Education, and Welfare - - Health - - - General
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