@TechReport{iza:izadps:dp6544,
            	author={Haan, Peter and Kemptner, Daniel and Uhlendorff, Arne},
            	title={Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models},
            	year={2012},
	month={May},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={6544},
            	url={https://www.iza.org/index.php/publications/dp6544},
	abstract={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.},
	keywords={dynamic discrete choice models;intertemporal labor supply behavior;Bayesian estimation},
            }