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.

人生历程包括相关事件和咒语的复杂模式。已知这些相关性的性质和强度取决于微观和宏观协变量。生命历程模型（例如事件历史分析和顺序分析）不能很好地处理决策过程的过程性和潜在性。我们认为隐马尔可夫模型满足生命过程模型的要求。为了说明它们的有用性，本研究将使用隐马尔可夫链对家庭形成的轨迹进行建模。我们使用了“世代与性别计划”中的数据来估计隐马尔可夫模型。结果表明，这种方法有可能揭示人生过程决策的基础机制，以及这些过程在性别和教育方面都不同。