Bayesian inference for duration data with unobserved and unknown heterogeneity: monte carlo evidence and an application
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Deutsch
- Bibliographic citation
-
IZA discussion papers ; 996
- Classification
-
Wirtschaft
- Event
-
Veröffentlichung
- (where)
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Bonn
- (who)
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Forschungsinstitut zur Zukunft der Arbeit GmbH
- (when)
-
2004
- Creator
-
Paserman, Daniele
- URN
-
urn:nbn:de:101:1-20100614619
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:39 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Paserman, Daniele
- Forschungsinstitut zur Zukunft der Arbeit GmbH
Time of origin
- 2004
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Unobserved heterogeneity in multiple-spell multiple-states duration models
Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models
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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