Alternative approaches for econometric analysis of panel count data using dynamic latent class models (with application to doctor visits data)

Health Economics
Judex Hyppolite, Pravin K Trivedi

Abstract

Cross-sectional latent class regression models, also known as switching regressions or hidden Markov models, cannot identify transitions between classes that may occur over time. This limitation can potentially be overcome when panel data are available. For such data, we develop a sequence of models that combine features of the static cross-sectional latent class (finite mixture) models with those of hidden Markov models. We model the probability of movement between categories in terms of a Markovian structure, which links the current state with a previous state, where state may refer to the category of an individual. This article presents a suite of mixture models of varying degree of complexity and flexibility for use in a panel count data setting, beginning with a baseline model which is a two-component mixture of Poisson distribution in which latent classes are fixed and permanent. Sequentially, we extend this framework (i) to allow the mixing proportions to be smoothly varying continuous functions of time-varying covariates, (ii) to add time dependence to the benchmark model by modeling the class-indicator variable as a first-order Markov chain and (iii) to extend item (i) by making it dynamic and introducing covariate dep...Continue Reading

References

Jul 31, 2002·Journal of Health Economics·Partha Deb, Pravin K Trivedi
Mar 7, 2006·Health Economics·Teresa Bago d'Uva

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Citations

Mar 18, 2015·PloS One·Ashkan Ebadi, Andrea Schiffauerova
Jun 27, 2014·Journal of Medical Systems·Juan José CubillasTomás Ureña

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