Bartolucci, Francesco and Lupparelli, Monia (2012): Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data.

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Abstract
In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and between clusters. For this aim we propose an approach based on nested hidden (latent) Markov chains, which are associated to every sample unit and to every cluster. The approach allows us to account for the mentioned forms of unobserved heterogeneity in a dynamic fashion; it also allows us to account for the correlation which may arise between the responses provided by the units belonging to the same cluster. Given the complexity in computing the manifest distribution of these response variables, we make inference on the proposed model through a composite likelihood function based on all the possible pairs of subjects within every cluster. The proposed approach is illustrated through an application to a dataset concerning a sample of Italian workers in which a binary response variable for the worker receiving an illness benefit was repeatedly observed.
Item Type:  MPRA Paper 

Original Title:  Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data 
Language:  English 
Keywords:  composite likelihood, EM algorithm, latent Markov model, pairwise likelihood 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C10  General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models; Multiple Variables > C33  Models with Panel Data; Longitudinal Data; Spatial Time Series 
Item ID:  40588 
Depositing User:  Francesco Bartolucci 
Date Deposited:  09. Aug 2012 12:45 
Last Modified:  15. Feb 2013 21:23 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/40588 