Artikel

A Bayesian Analysis of Unobserved Heterogeneity for Unemployment Duration Data in the Presence of Interval Censoring

In this paper, we discuss Bayesian inference of unobserved heterogeneity for unemployment duration data in the presence of right and interval-censoring, and non-proportionality. We employ accelerated failure time models with three different distributional assumptions: log-logistic, log-normal, and Weibull models, and use members of an exponential family of distributions for considering unobserved heterogeneity. We adopt a Bayesian approach, using Markov Chain Monte Carlo via WinBUGS software, to analyze the data. The proposed approach is illustrated using the unemployment duration data set of Iran in 2009. A sensitivity analysis using different latent variable models of the exponential family is also considered. After checking convergence, using the Gelman-Rubin diagnostic test, we compared different distributional assumptions using the DIC3 criterion. Our findings reveal significant discrepancies in unemployment duration based on different covariates for the sample population of Iran in 2009.

Language
Englisch

Bibliographic citation
Journal: International Econometric Review (IER) ; ISSN: 1308-8815 ; Volume: 6 ; Year: 2014 ; Issue: 1 ; Pages: 24-41 ; Ankara: Econometric Research Association (ERA)

Classification
Wirtschaft
Bayesian Analysis: General
Duration Analysis; Optimal Timing Strategies
Subject
Accelerated Failure Time Model
Bayesian Analysis
Interval Censoring
Kaplan-Meier Method
MCMC

Event
Geistige Schöpfung
(who)
Ganjali, Mojtaba
Baghfalaki, T.
Berridge, D.
Event
Veröffentlichung
(who)
Econometric Research Association (ERA)
(where)
Ankara
(when)
2014

DOI
doi:10.33818/ier.278029
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Ganjali, Mojtaba
  • Baghfalaki, T.
  • Berridge, D.
  • Econometric Research Association (ERA)

Time of origin

  • 2014

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