%0 Report
        %A Haan, Peter
%A Kemptner, Daniel
%A Uhlendorff, Arne
        %T Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models
        %D 2012
        %8 2012 May
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 6544
        %U https://www.iza.org/publications/dp6544
        %X Dynamic discrete choice models usually require a general specification of unobserved heterogeneity. In this paper, we apply Bayesian procedures as a numerical tool for the estimation of a female labor supply model based on a sample size which is typical for common household panels. We provide two important results for the practitioner: First, for a specification with a multivariate normal distribution for the unobserved heterogeneity, the Bayesian MCMC estimator yields almost identical results as a classical Maximum Simulated Likelihood (MSL) estimator. Second, we show that when imposing distributional assumptions which are consistent with economic theory, e.g. log-normally distributed consumption preferences, the Bayesian method performs well and provides reasonable estimates, while the MSL estimator does not converge. These results indicate that Bayesian procedures can be a beneficial tool for the estimation of dynamic discrete choice models.
        %K dynamic discrete choice models
%K intertemporal labor supply behavior
%K Bayesian estimation