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Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise

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  • Lijuan Liu
  • Feng Ding
  • Quanmin Zhu

Abstract

This paper considers the recursive identification problems for a class of multivariate autoregressive equation-error systems with autoregressive noise. By decomposing the system into several regressive identification subsystems, a maximum likelihood recursive generalised least squares identification algorithm is proposed to identify the parameter vectors in each subsystem. In addition, a multivariate recursive generalised least squares algorithm is derived as a comparison. The numerical simulation results indicate that the maximum likelihood recursive generalised least squares algorithm can effectively estimate the parameters of the multivariate autoregressive equation-error autoregressive systems and get more accurate parameter estimates than the multivariate recursive generalised least squares algorithm.

Suggested Citation

  • Lijuan Liu & Feng Ding & Quanmin Zhu, 2018. "Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(13), pages 2763-2775, October.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:13:p:2763-2775
    DOI: 10.1080/00207721.2018.1511873
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