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


Haan, Peter ; Kemptner, Daniel ; Uhlendorff, Arne



URL: http://ftp.iza.org/dp6544.pdf
Document Type: Working paper
Year of publication: 2012
The title of a journal, publication series: IZA Discussion Paper Series
Volume: 6544
Place of publication: Bonn
Publication language: English
Institution: School of Law and Economics > Alexander v. Humboldt Professor in Econometrics and Empirical Economics (Van den Berg -2016)
Subject: 330 Economics
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.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Haan, Peter ; Kemptner, Daniel ; Uhlendorff, Arne (2012) Bayesian procedures as a numerical tool for the estimation of dynamic discrete choice models. IZA Discussion Paper Series Bonn 6544 [Working paper]


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