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Bayesian estimation of Cox model with non-nested random effects: an application to the ratification of ILO conventions by developing countries

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  • Horney, Guillaume
  • Boockmann, Bernhard
  • Djurdjevic, Dragana
  • Laisney, François

Abstract

We use a multivariate hazard model for the analysis of data on the timing of ratifications of different conventions. The model accounts for two random effects, one at the country level and the other at the convention level. We use a semi-parametric Bayesian approach, based on the partial likelihood. Our findings confirm the results of preceding studies that ratification behaviour varies substantially across members states and conventions. Furthermore, the results yield insights on the impact of unobserved heterogeneity on the ratification process.

Suggested Citation

  • Horney, Guillaume & Boockmann, Bernhard & Djurdjevic, Dragana & Laisney, François, 2005. "Bayesian estimation of Cox model with non-nested random effects: an application to the ratification of ILO conventions by developing countries," ZEW Discussion Papers 05-23, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  • Handle: RePEc:zbw:zewdip:3180
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    References listed on IDEAS

    as
    1. Ridder, Geert & Tunali, Insan, 1999. "Stratified partial likelihood estimation," Journal of Econometrics, Elsevier, vol. 92(2), pages 193-232, October.
    2. Guillaume Horny, 2009. "Inference in mixed proportional hazard models with K random effects," Statistical Papers, Springer, vol. 50(3), pages 481-499, June.
    3. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460 Elsevier.
    4. Bolstad W. M & Manda S. O, 2001. "Investigating Child Mortality in Malawi Using Family and Community Random Effects: A Bayesian Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 12-19, March.
    5. Bo E. Honoré, 1993. "Identification Results for Duration Models with Multiple Spells," Review of Economic Studies, Oxford University Press, vol. 60(1), pages 241-246.
    6. Bernhard Boockmann, 2001. "The ratification of ILO conventions: A hazard rate analysis," Economics and Politics, Wiley Blackwell, vol. 13(3), pages 281-309, November.
    7. Germán Rodríguez & Noreen Goldman, 2001. "Improved estimation procedures for multilevel models with binary response: a case-study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 339-355.
    8. Lancaster, Tony, 1979. "Econometric Methods for the Duration of Unemployment," Econometrica, Econometric Society, vol. 47(4), pages 939-956, July.
    9. Chris Elbers & Geert Ridder, 1982. "True and Spurious Duration Dependence: The Identifiability of the Proportional Hazard Model," Review of Economic Studies, Oxford University Press, vol. 49(3), pages 403-409.
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    Citations

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    Cited by:

    1. Guillaume Horny, 2009. "Inference in mixed proportional hazard models with K random effects," Statistical Papers, Springer, vol. 50(3), pages 481-499, June.
    2. Guillaume Horny & Rute Mendes & Gerard J. van den Berg, 2012. "Job Durations With Worker- and Firm-Specific Effects: MCMC Estimation With Longitudinal Employer--Employee Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 468-480, March.
    3. Guillaume Horny & Rute Mendes & Gerard J. Van den Berg, 2006. "Job mobility in Portugal: a Bayesian study with matched worker-firm data," Working Papers of BETA 2006-32, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    4. Konstantinos, Pouliakas & Ioannis, Theodossiou, 2010. "An Inquiry Into the Theory, Causes and Consequences of Monitoring Indicators of Health and Safety At Work," MPRA Paper 20336, University Library of Munich, Germany.

    More about this item

    Keywords

    gibbs sampling; partial likelihood; frailties; duration analysis;

    JEL classification:

    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • O19 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - International Linkages to Development; Role of International Organizations
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • J80 - Labor and Demographic Economics - - Labor Standards - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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