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Bayesian procedures as a numerical tool for the estimation of dynamic discrete choice models
Haan, Peter
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Kemptner, Daniel
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Uhlendorff, Arne
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URL:
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http://ftp.iza.org/dp6544.pdf
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Dokumenttyp:
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Arbeitspapier
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Erscheinungsjahr:
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2012
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Titel einer Zeitschrift oder einer Reihe:
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IZA Discussion Paper Series
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Band/Volume:
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6544
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Ort der Veröffentlichung:
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Bonn
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Rechtswissenschaft und Volkswirtschaftslehre > Alexander v. Humboldt Professor in Econometrics and Empirical Economics (van den Berg 2009-2016)
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Fachgebiet:
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330 Wirtschaft
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Abstract:
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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.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
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