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

Author

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  • Mojtaba Ganjali

    (Department of Statistics, Shahid Beheshti University, Tehran, Iran)

  • T. Baghfalaki

    (Department of Statistics, Shahid Beheshti University, Tehran, Iran)

  • D. Berridge

    (Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster, UK.)

Abstract

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.

Suggested Citation

  • Mojtaba Ganjali & T. Baghfalaki & D. Berridge, 2014. "A Bayesian Analysis of Unobserved Heterogeneity for Unemployment Duration Data in the Presence of Interval Censoring," International Econometric Review (IER), Econometric Research Association, vol. 6(1), pages 24-41, April.
    • Handle: RePEc:erh:journl:v:6:y:2014:i:1:p:24-41
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    References listed on IDEAS

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    5. Wei Pan & Thomas A. Louis, 2000. "A Linear Mixed-Effects Model for Multivariate Censored Data," Biometrics, The International Biometric Society, vol. 56(1), pages 160-166, March.
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    More about this item

    Keywords

    Accelerated Failure Time Model; Bayesian Analysis; Interval Censoring; Kaplan-Meier Method; MCMC.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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