Linear system identification from nonstationary cross-sectional data

The identification of time-invariant linear stochastic systems from cross-sectional data on nonstationary system behavior is considered. A strong consistency and asymptotic normality result for maximum likelihood and prediction error estimates of the system parameters, system and measurement noise covariances, and the initial state covariance is proven. A new tdentifiability property for the system model is defined and appears in the set of conditions for this result. The nonstationary stochastic realization (i.e, covariance factorization) theorem in [1] provides sufficient conditions for the identifiability property to hold. An application illusrating the use of a computer program implementing the identification method is presented.