Bayesian procedures as a numerical tool for the estimation of dynamic discrete choice models

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." (Author's abstract, IAB-Doku) ((en))

Cite article

Haan, P., Kemptner, D. & Uhlendorff, A. (2012): Bayesian procedures as a numerical tool for the estimation of dynamic discrete choice models. (DIW-Diskussionspapiere 1210), Berlin, 18 p.