Multivariate time series analysis with state space models

AbstractThe paper proposes a method for estimating linear, time-invariant state space models from multiple time series data. The approach is based on stochastic realization theory. The coefficient matrices of the state space model are derived from the estimated Markov parameters that are associated with the different system inputs, such as lagged endogenous variables, observable exogenous variables, and unobservable noise