Bayesian Analysis of A Model with Binary Selectivity and Ordered Outcomes
نویسندگان
چکیده
This paper presents a Bayesian analysis to estimate parameters and latent variables in an ordered censored sample selection model. After reparameterization which greatly improves convergence rate and uses specially designed priors, efficient Gibbs sampler is set up with conjugate conditional posteriors. Then a numerical study is conducted to evaluate the convergence rate of MCMC estimates, and compare our Bayesian method and FIML estimation to show its accuracy and efficiency. Finally, an application to mental illness and labor market costs indicates that Bayesian method can provide sufficient information.
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