B1496
Title: Copula state-space models with several latent variables
Authors: Ariane Hanebeck - Technical University of Munich (Germany) [presenting]
Claudia Czado - Technische Universitaet Muenchen (Germany)
Abstract: A state-space model with several latent variables is proposed which uses copulas to get away from the assumption of Gaussian noise. State-space models are an important tool for analyzing time series. They assume that given observations depend on unobserved states over a so-called observation equation. The temporal behavior of the states is explained by the state equation. In many applications, it is assumed that the noise follows a Gaussian distribution. However, this assumption is often not met by real datasets. Copulas are an adequate tool to lift the Gaussian assumption and extend state-space models to very flexible models. For multiple observations with one latent variable, this model was already considered. The natural extension of this model is to allow for more than one latent variable. A Bayesian approach and MCMC-sampling are used to fit the model. Simulated data and real data examples are used to demonstrate the model fit.
