Mechanisms of family formation: an application of Hidden Markov Models to a life course process

Sapphire Yu Han*, Aart C. Liefbroer, Cees H. Elzinga

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Life courses consist of complex patterns of correlated events and spells. The nature and strength of these correlations is known to depend on both micro- and macro- covariates. Life-course models such as event-history analysis and sequence analysis are not well equipped to deal with the processual and latent character of the decision- making process. We argue that Hidden Markov Models satisfy the requirements of a life course model. To illustrate their usefulness, this study will use Hidden Markov chains to model trajectories of family formation. We used data from the Generations and Gender Programme to estimate Hidden Markov Models. The results show the potential of this approach to unravel the mechanisms underlying life-course decision making and how these processes differ both by gender and education.

Original languageEnglish
Article number100265
Pages (from-to)1-12
Number of pages12
Early online date29 Jul 2019
Publication statusPublished - Mar 2020

Bibliographical note

Funding Information:
The research leading to these results has received funding from the European Research Council under Grant Agreement No 324178FP7/ERC (Project: Contexts of Opportunity, PL Aart C. Liefbroer)

Publisher Copyright:
© 2019


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