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STRUCTURAL, DYNAMIC MODELLING IN UNOBSERVABLE SPACES OF COVARIANCE‐STATIONARY STOCHASTIC PROCESSES
Author(s) -
Otter Pieter W.
Publication year - 1988
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1988.tb00453.x
Subject(s) - identifiability , unobservable , mathematics , covariance , kalman filter , consistency (knowledge bases) , rank (graph theory) , state vector , state space , errors in variables models , state space representation , econometrics , statistics , algorithm , physics , geometry , classical mechanics , combinatorics
In 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 model parameters.