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Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates

Francesco Bartolucci, Alessio Farcomeni and Fulvia Pennoni

MPRA Paper from University Library of Munich, Germany

Abstract: We provide a comprehensive overview of latent Markov (LM) models for the analysis of longitudinal data. The main assumption behind these models is that the response variables are conditionally independent given a latent process which follows a first-order Markov chain. We first illustrate the more general version of the LM model which includes individual covariates. We then illustrate several constrained versions of the general LM model, which make the model more parsimonious and allow us to consider and test hypotheses of interest. These constraints may be put on the conditional distribution of the response variables given the latent process (measurement model) or on the distribution of the latent process (latent model). For the general version of the model we also illustrate in detail maximum likelihood estimation through the Expectation-Maximization algorithm, which may be efficiently implemented by recursions known in the hidden Markov literature. We discuss about the model identifiability and we outline methods for obtaining standard errors for the parameter estimates. We also illustrate methods for selecting the number of states and for path prediction. Finally, we illustrate Bayesian estimation method. Models and related inference are illustrated by the description of relevant socio-economic applications available in the literature.

Keywords: EM algorithm; Bayesian framework; Forward-Backward recursions; Hidden Markov models; Measurement errors; Panel data; Unobserved heterogeneity (search for similar items in EconPapers)
JEL-codes: C10 C33 (search for similar items in EconPapers)
Date: 2012-04-13
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (3) Track citations by RSS feed

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Journal Article: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates (2014) Downloads
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