A discussion on hidden Markov models for life course data

TitleA discussion on hidden Markov models for life course data
Publication TypeBook Chapter
Year of Publication2016
AuthorsBolano, D, Berchtold, A, Ritschard, G
Book TitleProceedings of the International Conference on Sequence Analysis and Related Methods (LaCOSA II)
Place PublishedLausanne, Switzerland
Keywordshidden Markov model, Life course approach, sequence analysis

This is an introduction on discrete-time Hidden Markov models (HMM) for longitudinal data analysis in population and life course studies. In the Markovian perspective, life trajectories are considered as the result of a stochastic process in which the probability of occurrence of a particular state or event depends on the sequence of states observed so far. Markovian models are used to analyze the transition process between successive states. Starting from the traditional formulation of a first-order discrete-time Markov chain where each state is liked to the next one, we present the hidden Markov models where the current response is driven by a latent variable that follows a Markov process. The paper presents also a simple way of handling categorical covariates to capture the effect of external factors on the transition probabilities and existing software are briefly overviewed. Empirical illustrations using data on self reported health demonstrate the relevance of the different extensions for life course analysis.

Refereed DesignationRefereed