AbstractIn this paper a structural, stationary version of the well‐known state‐space model is used to model covariance‐stationary stochastic processes. The identifiability of the model parameters is discussed and a rank condition for local parameter identifiability is given. Ljung's results on prediction‐error estimation are used to establish strong consistency and asymptotic efficiency of the non‐linear ML‐estimates obtained from dependent observations. It turns out that the model can be identified by using simultaneously the steady‐state Kalman filter for the unobservable state vector and the prediction‐error estimation method for the m
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