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

Francesco Billari, Johannes Fürnkranz and Alexia Fürnkranz-Prskawetz

No WP-2000-010, MPIDR Working Papers from Max Planck Institute for Demographic Research, Rostock, Germany

Abstract: 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]

JEL-codes: J1 Z0 (search for similar items in EconPapers)
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.demogr.mpg.de/Papers/Working/wp-2000-010.pdf (application/pdf)
https://www.ofai.at/publications (text/html)

Related works:
Journal Article: Timing, Sequencing, and Quantum of Life Course Events: A Machine Learning Approach (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dem:wpaper:wp-2000-010

DOI: 10.4054/MPIDR-WP-2000-010

Access Statistics for this paper

More papers in MPIDR Working Papers from Max Planck Institute for Demographic Research, Rostock, Germany
Bibliographic data for series maintained by Peter Wilhelm ().