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System Identification by Approximate Realization

In: Predictability and Nonlinear Modelling in Natural Sciences and Economics

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  • Christiaan Heij

    (Erasmus University Rotterdam, Econometrics Institute)

Abstract

We discuss the use of state variables in time series modelling. Current procedures are mostly based on realization theory. First certain parameters are estimated which describe the process, e.g., the systems impulse response or autocorrelations. These parameters are then transformed into an approximate state space model. In this note we suggest an opposite procedure. First an approximate state trajectory is estimated, and in a second stage a corresponding state space model is determined. This method allows to infer several structural properties of the process from the observed data, in particular the dynamical structure (length of the involved time lags), causality (which variables are inputs and outputs), and the noise (whether it is stochastic or not).

Suggested Citation

  • Christiaan Heij, 1994. "System Identification by Approximate Realization," Springer Books, in: J. Grasman & G. van Straten (ed.), Predictability and Nonlinear Modelling in Natural Sciences and Economics, pages 312-321, Springer.
    • Handle: RePEc:spr:sprchp:978-94-011-0962-8_26
    DOI: 10.1007/978-94-011-0962-8_26
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