Mauro Laudicella () and Paolo Li Donni ()
Additional contact information
Mauro Laudicella: University of Southern Denmark, DaCHE - Danish Centre for Health Economics, Postal: DaCHE - Danish Centre for Health Economics, Institut for Sundhedstjenesteforskning, Syddansk Universitet, J.B. Winsløws Vej 9B, 2. sal, DK-5000 Odense, Denmark
Paolo Li Donni: University of Palermo, Department of Economics, Business and Statistics, Postal: and University of Southern Denmark, DaCHE - Danish Centre for Health Economics.
Abstract: 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.
Keywords: mixture distributions; hidden Markov models; panel data; primary care; secondary care; Denmark healthcare.
35 pages, March 25, 2021
Full text files
DaCHE_Discussion_Paper_2021_1.pdf Full text
Questions (including download problems) about the papers in this series should be directed to Christian Volmar Skovsgaard ()
Report other problems with accessing this service to Sune Karlsson ().
RePEc:hhs:sduhec:2021_001This page generated on 2024-09-13 22:17:03.