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Bayesian Survival Modelling of University Outcomes


  • Vallejos, Catalina
  • Steel, Mark F. J.


The aim of this paper is to model the length of registration at university and its associated academic outcome for undergraduate students at the Pontificia Universidad Cat´olica de Chile. Survival time is defined as the time until the end of the enrollment period, which can relate to different reasons - graduation or two types of dropout - that are driven by different processes. Hence, a competing risks model is employed for the analysis. The issue of separation of the outcomes (which precludes maximum likelihood estimation) is handled through the use of Bayesian inference with an appropriately chosen prior. We are interested in identifying important determinants of university outcomes and the associated model uncertainty is formally addressed through Bayesian model averaging. The methodology introduced for modelling university outcomes is applied to three selected degree programmes, which are particularly affected by dropout and late graduation.

Suggested Citation

  • Vallejos, Catalina & Steel, Mark F. J., 2014. "Bayesian Survival Modelling of University Outcomes," MPRA Paper 57185, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:57185

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    References listed on IDEAS

    1. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    2. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    3. Nicholas G. Polson & James G. Scott & Jesse Windle, 2013. "Bayesian Inference for Logistic Models Using Pólya--Gamma Latent Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1339-1349, December.
    4. Chen, Ming-Hui & Ibrahim, Joseph G. & Kim, Sungduk, 2008. "Properties and Implementation of Jeffreys’s Prior in Binomial Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1659-1664.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Poirier, Dale, 1994. "Jeffreys' prior for logit models," Journal of Econometrics, Elsevier, vol. 63(2), pages 327-339, August.
    7. Marc A. Scott & Benjamin B. Kennedy, 2005. "Pitfalls in Pathways: Some Perspectives on Competing Risks Event History Analysis in Education Research," Journal of Educational and Behavioral Statistics, , vol. 30(4), pages 413-442, December.
    8. Elena Arias & Catherine Dehon, 2011. "The Roads to Success: Analyzing Dropout and Degree Completion at University," Working Papers ECARES ECARES 2011-025, ULB -- Universite Libre de Bruxelles.
    9. Judith D. Singer & John B. Willett, 1993. "It’s About Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events," Journal of Educational and Behavioral Statistics, , vol. 18(2), pages 155-195, June.
    10. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
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    Cited by:

    1. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
    2. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2018. "The economics of university dropouts and delayed graduation: a survey," GLO Discussion Paper Series 189, Global Labor Organization (GLO).

    More about this item


    Bayesian model averaging; Competing risks; Outcomes separation; Proportional Odds model; University dropout;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • 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
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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