Estimating the Demand for Health Care with Panel Data: A Semiparametric Bayesian Approach
AbstractThis 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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Bibliographic InfoPaper provided by The University of Sheffield, Department of Economics in its series Working Papers with number 2003005.
Length: 26 pages
Date of creation: Oct 2003
Date of revision: Oct 2003
random efects model; Dirichlet process prior; MCMC;
Other versions of this item:
- Markus Jochmann & Roberto León-González, 2004. "Estimating the demand for health care with panel data: a semiparametric Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 1003-1014.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- I10 - Health, Education, and Welfare - - Health - - - General
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