IDEAS home Printed from
   My bibliography  Save this paper

Timing, sequencing and quantum of life course events: a machine learning approach


  • Francesco C. Billari

    (Max Planck Institute for Demographic Research, Rostock, Germany)

  • Johannes Fürnkranz
  • Alexia Prskawetz

    (Max Planck Institute for Demographic Research, Rostock, Germany)


In this methodological paper we discuss and apply machine learning tech-niques, a core research area in the artificial intelligence literature, to analyse simultaneously timing, sequencing, and quantum of life course events from a comparative perspective. We outline the need for techniques which allow the adoption of a holistic approach to the analysis of life courses, illustrating the specific case of the transition to adulthood. We briefly introduce machine learning algorithms to build decision trees and rule sets and then apply such algorithms to delineate the key features which distinguish Austrian and Ital-ian pathways to adulthood, using Fertility and Family Survey data. The key role of sequencing and synchronisation between events emerges clearly from the methodology used. [AUTHORS]

Suggested Citation

  • Francesco C. Billari & Johannes Fürnkranz & Alexia Prskawetz, 2000. "Timing, sequencing and quantum of life course events: a machine learning approach," MPIDR Working Papers WP-2000-010, Max Planck Institute for Demographic Research, Rostock, Germany.
  • Handle: RePEc:dem:wpaper:wp-2000-010

    Download full text from publisher

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Courgeau, Daniel & Lelievre, Eva, 1993. "Event History Analysis in Demography," OUP Catalogue, Oxford University Press, number 9780198287384, June.
    2. Lillard, Lee A., 1993. "Simultaneous equations for hazards : Marriage duration and fertility timing," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 189-217, March.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Gabadinho, Alexis & Ritschard, Gilbert & Müller, Nicolas S & Studer, Matthias, 2011. "Analyzing and Visualizing State Sequences in R with TraMineR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i04).

    More about this item

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General


    Access and download statistics


    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:dem:wpaper:wp-2000-010. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Wilhelm). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.