TY  - RPRT
        AU  - Paserman, M. Daniele
        TI  - Bayesian Inference for Duration Data with Unobserved and Unknown Heterogeneity: Monte Carlo Evidence and an Application
        PY  - 2004/Jan/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 996
        UR  - https://www.iza.org/index.php/publications/dp996
        AB  - 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.
        KW  - duration data
KW  - Dirichlet process
KW  - Bayesian inference
KW  - Markov chain Monte Carlo simulation
        ER  -