Bayesian Inference for Duration Data with Unobserved and Unknown Heterogeneity: Monte Carlo Evidence and an Application

45 Pages Posted: 13 Jan 2004

See all articles by M. Daniele Paserman

M. Daniele Paserman

Hebrew University of Jerusalem - Department of Economics; Centre for Economic Policy Research (CEPR); IZA Institute of Labor Economics

Date Written: January 2004

Abstract

This paper describes a semiparametric Bayesian method for analyzing duration data. The proposed estimator specifies a complete functional form for duration spells, but allows flexibility by introducing an individual heterogeneity term, which follows a Dirichlet mixture distribution. I show how to obtain predictive distributions for duration data that correctly account for the uncertainty present in the model. I also directly compare the performance of the proposed estimator with Heckman and Singer's (1984) Non Parametric Maximum Likelihood Estimator (NPMLE).

The methodology is applied to the analysis of youth unemployment spells. Compared to the NPMLE, the proposed estimator reflects more accurately the uncertainty surrounding the heterogeneity distribution.

Keywords: Duration data, dirichlet process, bayesian inference, markov chain Monte Carlo simulation

JEL Classification: C11, C41

Suggested Citation

Paserman, M. Daniele, Bayesian Inference for Duration Data with Unobserved and Unknown Heterogeneity: Monte Carlo Evidence and an Application (January 2004). Available at SSRN: https://ssrn.com/abstract=485624

M. Daniele Paserman (Contact Author)

Hebrew University of Jerusalem - Department of Economics ( email )

Mount Scopus
Jerusalem, 91905
Israel
+972 2 588 3365 (Phone)
+972 2 581 6071 (Fax)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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