Arbeitspapier
Bayesian inference for duration data with unobserved and unknown heterogeneity : Monte Carlo evidence and an application
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.
- Language
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Englisch
- Bibliographic citation
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Series: IZA Discussion Papers ; No. 996
- Classification
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Wirtschaft
Duration Analysis; Optimal Timing Strategies
Bayesian Analysis: General
- Subject
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duration data
Dirichlet process
Bayesian inference
Markov chain Monte Carlo simulation
Statistische Bestandsanalyse
Nichtparametrisches Verfahren
Bayes-Statistik
Maximum-Likelihood-Methode
Schätzung
Jugendarbeitslosigkeit
Theorie
Vereinigte Staaten
- Event
-
Geistige Schöpfung
- (who)
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Paserman, Marco Daniele
- Event
-
Veröffentlichung
- (who)
-
Institute for the Study of Labor (IZA)
- (where)
-
Bonn
- (when)
-
2004
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Paserman, Marco Daniele
- Institute for the Study of Labor (IZA)
Time of origin
- 2004
Other Objects (12)
Bayesian inference for duration data with unobserved and unknown heterogeneity: monte carlo evidence and an application
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Bayesian inference for duration data with unobserved and unknown heterogeneity: monte carlo evidence and an application
Separability of Duration Dependence and Unobserved Heterogeneity
The Unobserved Heterogeneity Distribution in Duration Analysis
A Bayesian Analysis of Unobserved Heterogeneity for Unemployment Duration Data in the Presence of Interval Censoring
Why Bayesian inference?
Bayesian Practical Inference
Unobserved heterogeneity in multiple-spell multiple-states duration models
Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models
Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework
Bayesian inference for continuous-time hidden Markov models with an unknown number of states
A Bayesian Analysis of Unobserved Component Models using Ox
Panel data models with nonadditive unobserved heterogeneity: Estimation and inference
Bayesian inference for duration data with unobserved and unknown heterogeneity: monte carlo evidence and an application
Separability of Duration Dependence and Unobserved Heterogeneity
The Unobserved Heterogeneity Distribution in Duration Analysis
A Bayesian Analysis of Unobserved Heterogeneity for Unemployment Duration Data in the Presence of Interval Censoring
Why Bayesian inference?
Bayesian Practical Inference
Unobserved heterogeneity in multiple-spell multiple-states duration models
Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models
Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework
Bayesian inference for continuous-time hidden Markov models with an unknown number of states
A Bayesian Analysis of Unobserved Component Models using Ox