Implementation of Multivariate Continuous-Time ARMA Models
The multivariate continuous-time ARMA model is a tool to capture the relationship between multivariate time series. In this chapter, a particular computational implementation of a stationary normal multivariate CARMA model is illustrated. A review of a parametric setup is shown. Data are assumed to be observed at irregular non-synchronous discrete time points. The computational approach for calculating the likelihood is based on a state-space form and the Kalman filter. Interpretation of the CARMA models is discussed. The computational algorithms have been implemented in R packages. Examples of a simulated and real data are shown.
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