Identification and estimation of discrete state-vector models with stochastic inputs

The study deals with the identification and estimation of the unknown parameters of an ‘extended’ state-vector model, in which stochastic input variables are treated as ‘state’-variables and the observed input-values as ‘output’-values of the model.

A parameter identifiability criterion, based on Fisher's information matrix, is applied to the model and a general ML-estimation procedure is given. If a certain restriction on the covariance-matrix of the state-vector is placed, the ML-procedure simplifies and coincides with an operational method, called the Lisrel procedure. This procedure provides also a test for parameter identifiability.