Predictor input selection for direct identification in dynamic networks

In the literature methods have been proposed which enable consistent estimates of modules embedded in complex dynamic networks. In this paper the network extension of the so called closed-loop Direct Method is investigated. Currently, for this method the variables which must be included in the predictor model are not considered as a user choice. In this paper it is shown that there is some freedom as to which variables to include in the predictor model as inputs, and still obtain consistent estimates of the module of interest. Conditions on this choice of predictor inputs are presented.