B0362
Title: Multivariate analysis of mortality data using time-varying copula state space models
Authors: Ariane Hanebeck - Technical University of Munich (Germany) [presenting]
Claudia Czado - Technical University of Munich (Germany)
Abstract: The aim is to model and quantify the dependencies between five causes of death, conditional on the weekly number of COVID-19 deaths. Based on the given time series data, the use of the model class of copula state space models is proposed. The associated latent variable, which is assumed to be independent of the number of Covid deaths, can be interpreted as a general driving factor of the causes of death. The dependence between the causes and the latent state however is modeled as varying with the number of Covid deaths. Using this approach, the data in the pre-Covid and post-Covid time can be modelled within one setup. This leads to a very flexible model allowing for the time dynamics between the causes of death. For the inference, a Bayesian approach is chosen. Due to the high nonlinearity and non-Gaussianity, a Hamiltonian Monte Carlo algorithm is used to sample from the posterior density.
