Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models

Peter Haan, Daniel Kemptner (daniel.kemptner@web.de) and Arne Uhlendorff

No 6544, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 (search for similar items in EconPapers)
JEL-codes: C11 C25 J22 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2012-05
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published - published in: Empirical Economics, 2015, 49, 1123-1141

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Working Paper: Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models (2012)
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