Publication


Han Sapphire Yu, Aart C. Liefbroer, Cees H. Elzinga
Mechanisms of family formation: An application of Hidden Markov Models to a life course process
Advances in Life Course Research, 2019
URL, JabRef BibTex, Abstract
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 the 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.

Reference


@article{Yu2019a,
  author = {Han Sapphire Yu, Aart C. Liefbroer, Cees H. Elzinga},
  title = {Mechanisms of family formation: An application of Hidden Markov Models to a life course process},
  year = {2019},
  journal = {Advances in Life Course Research},
  month = {Jul},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S1040260818300807},
  timestamp = {03.10.2019},
  abstract = {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 the 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.}
}
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