Search results for "latent Markov model"

Patterns of poverty among elderly Americans: a Latent Class Markov Model

2017

ABSTRACTThis article studies poverty persistence and the role of social security programmes on poverty among elderly in the US. We use a Latent Markov model to disentangle unobserved heterogeneity and state dependence. Because of its dynamic nature, unobserved heterogeneity is modelled to vary over time. This allows to capture different latent states of poverty that change over time. Result indicates the existence of three unobserved types evolving over time according to their propensity to be poor. Moreover, a strong persistence in poverty especially for women, individuals living alone and ethnic minorities is found. Finally, the estimates indicate that giving social assistance tends to re…

Uncontrolled diabetes and health care utilisation:A bivariate latent Markov model approach

2018

Although uncontrolled diabetes (UD) or poor glycaemic control is a widespread condition with potentially life-threatening consequences, there is sparse evidence of its effects on health care utilisation. We jointly model the propensities to consume health care and UD by employing an innovative bivariate latent Markov model that allows for dynamic unobserved heterogeneity, movements between latent states and the endogeneity of UD. We estimate the effects of UD on primary and secondary health care consumption using a panel dataset of rich administrative records from Spain and measure UD using a biomarker. We find that, conditional on time-varying unobservables, UD does not have a statisticall…

The dynamic interdependence in the demand of primary and emergency secondary care: A hidden Markov approach

2021

This paper develops an extension of the class of finite mixture models for longitudinal count data to the bivariate case by using a trivariate reduction technique and a hidden Markov chain approach. The model allows for disentangling unobservable time-varying heterogeneity from the dynamic effect of utilisation of primary and secondary care and measuring their potential substitution effect. Three points of supports adequately describe the distribution of the latent states suggesting the existence of three profiles of low, medium and high users who shows persistency in their behaviour, but not permanence as some switch to their neighbour's profile.

Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data

2018

Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an …

The unobserved pattern of material hardship and health among older Americans

2019

This paper investigates the relationship between self-reported health and material hardship among older Americans. Differently from income-based measures, material hardship provides a more specific description of the concrete adversities faced by the elderly. We have used the last six waves of the Health and Retirement Study to explore the relative contributions of state dependence, unobserved heterogeneity and time-specific shocks on reporting poor health, experiencing food insecurity and medication cutbacks. We have used a Latent Markov model to estimate a multivariate non-linear system of equations for panel data allowing time-varying unobserved heterogeneity. Our results reveal a high s…

Statistical analysis of life sequence data

2016

Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R

2019

Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariate…

Short-run and long-run persistence of bad health among elderly

2019

We study the health dynamics among older Americans using ten waves of the Health and Retirement Study following a spell-approach and a regression-based approach. The former is fully non parametric synthesizing the sequences of health status into a Health Persistence Index. The latter approach relies on a Latent Markov (LM) model capturing persistence in poor health by modelling time-varying unobserved heterogeneity. Our results show that only few elders experiences persistently a poor health status. The higher values of the index are consistently observed with the main socio-demo-economic risk factors. Moreover LM model indicates the existence of three unobserved groups differing in their p…