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Title:Bayesian procedures as a numerical tool for the estimation of dynamic discrete choice models PDF Logo
Authors:Haan, Peter
Kemptner, Daniel
Uhlendorff, Arne
Issue Date:2012
Series/Report no.:Discussion Paper series, Forschungsinstitut zur Zukunft der Arbeit 6544
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.
Subjects:Bayesian estimation
dynamic discrete choice models
intertemporal labor supply behavior
JEL:C11
C25
J22
Persistent Identifier of the first edition:urn:nbn:de:101:1-201208175234
Document Type:Working Paper
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)
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