IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/57185.html

Some searches may not work properly. We apologize for the inconvenience.

   My bibliography  Save this paper

Bayesian Survival Modelling of University Outcomes

Author

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

Abstract

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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/57185/1/MPRA_paper_57185.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Poirier, Dale, 1994. "Jeffreys' prior for logit models," Journal of Econometrics, Elsevier, vol. 63(2), pages 327-339, August.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Diego Opazo & Sebastián Moreno & Eduardo Álvarez-Miranda & Jordi Pereira, 2021. "Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities," Mathematics, MDPI, vol. 9(20), pages 1-27, October.
    2. Samuel I. Watson & Richard J. Lilford & Jianxia Sun & Julian Bion, 2021. "Estimating the effect of health service delivery interventions on patient length of stay: A Bayesian survival analysis approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1164-1186, November.
    3. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    4. Moritz Berger & Thomas Welchowski & Steffen Schmitz-Valckenberg & Matthias Schmid, 2019. "A classification tree approach for the modeling of competing risks in discrete time," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 965-990, December.
    5. 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).
    6. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2022. "The determinants of university dropout: A review of the socio-economic literature," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    2. Jesus Crespo Cuaresma & Bettina Grün & Paul Hofmarcher & Stefan Humer & Mathias Moser, 2015. "A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications," Department of Economics Working Papers wuwp193, Vienna University of Economics and Business, Department of Economics.
    3. Aart Kraay & Norikazu Tawara, 2013. "Can specific policy indicators identify reform priorities?," Journal of Economic Growth, Springer, vol. 18(3), pages 253-283, September.
    4. Crespo Cuaresma, Jesus & von Schweinitz, Gregor & Wendt, Katharina, 2019. "On the empirics of reserve requirements and economic growth," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 253-274.
    5. Bruns, Stephan B. & Ioannidis, John P.A., 2020. "Determinants of economic growth: Different time different answer?," Journal of Macroeconomics, Elsevier, vol. 63(C).
    6. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).
    7. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    8. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    9. Kourtellos, Andros & Marr, Christa & Tan, Chih Ming, 2016. "Robust determinants of intergenerational mobility in the land of opportunity," European Economic Review, Elsevier, vol. 81(C), pages 132-147.
    10. Hofmarcher, Paul & Crespo Cuaresma, Jesus & Grün, Bettina & Humer, Stefan & Moser, Mathias, 2018. "Bivariate jointness measures in Bayesian Model Averaging: Solving the conundrum," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 150-165.
    11. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
    12. Man, Georg, 2015. "Competition and the growth of nations: International evidence from Bayesian model averaging," Economic Modelling, Elsevier, vol. 51(C), pages 491-501.
    13. Paul Hofmarcher & Jesús Crespo Cuaresma & Bettina Grün & Kurt Hornik, 2015. "Last Night a Shrinkage Saved My Life: Economic Growth, Model Uncertainty and Correlated Regressors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 133-144, March.
    14. Salimans, Tim, 2012. "Variable selection and functional form uncertainty in cross-country growth regressions," Journal of Econometrics, Elsevier, vol. 171(2), pages 267-280.
    15. Leamer, Edward E., 2016. "S-values and Bayesian weighted all-subsets regressions," European Economic Review, Elsevier, vol. 81(C), pages 15-31.
    16. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    17. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2011. "Market Freedom and the Global Recession," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 59(1), pages 111-135, April.
    18. León-González, Roberto & Montolio, Daniel, 2015. "Endogeneity and panel data in growth regressions: A Bayesian model averaging approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 23-39.
    19. Hasan, Iftekhar & Horvath, Roman & Mares, Jan, 2020. "Finance and wealth inequality," Journal of International Money and Finance, Elsevier, vol. 108(C).
    20. Mariam Camarero & Sergi Moliner & Cecilio Tamarit, 2021. "Is there a euro effect in the drivers of US FDI? New evidence using Bayesian model averaging techniques," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 157(4), pages 881-926, November.

    More about this item

    Keywords

    Bayesian model averaging; Competing risks; Outcomes separation; Proportional Odds model; University dropout;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:57185. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.