Local and global identification and strong consistency in time series models

Abstract We define local and global identification with respect to a sequence of criterion functions and show how global identification is related to a proof of strong consistency. We also show that local identification of the parameters is needed to ensure that common estimation procedures are well defined. We illustrate the results by an application to the simultaneous ARMAX model. Finally, we motivate and generalize a local identification result for dynamic, simultaneous equations models given by Hatanaka (1976).

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