A novel flexible class of multivariate nonlinear non-Gaussian state space models, based on copulas, is proposed. Specifically, it assumed that the observation equation and are defined by copula families not necessarily equal. Inference performed within Bayesian framework, using Hamiltonian Monte Carlo method. Simulation studies show proposed copula-based approach extremely flexible, since able ...

We introduce a general approach which unifies some previous attempts for modeling the dynamic of multivariate time series or regression analysis when data are mixed type (binary/count/continuous). Our is quite flexible since conditionally on past values, each coordinate at t can have distribution compatible with standard univariate model such as GARCH, ARMA, INGARCH logistic models whereas valu...