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

Peter Haan, Daniel Kemptner () and Arne Uhlendorff

No 1210, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

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: Bayesian Estimation; Dynamic Discrete Choice Models; Intertemporal Labor Supply Behavior (search for similar items in EconPapers)
JEL-codes: C11 C25 J22 (search for similar items in EconPapers)
Pages: 18 p.
Date: 2012
New Economics Papers: this item is included in nep-dcm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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