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Iterative Least Squares Estimation and Identification of the Transfer Function Model

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  • Daniel Muller
  • William W. S. Wei

Abstract

The ordinary least squares method is the most commonly used estimation procedure in statistics but estimates of the input and output parameters through this method for transfer function models are not necessarily consistent. An iterative regression procedure is proposed to produce consistent estimates. Consistent moment estimates are also given. On the basis of these consistent estimates a method of model specification is proposed. An example is given to illustrate the procedure

Suggested Citation

  • Daniel Muller & William W. S. Wei, 1997. "Iterative Least Squares Estimation and Identification of the Transfer Function Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(6), pages 579-592, November.
    • Handle: RePEc:bla:jtsera:v:18:y:1997:i:6:p:579-592
    DOI: 10.1111/1467-9892.00069
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    Cited by:

    1. M. Azimmohseni & M. Khalafi & M. Kordkatuli, 2019. "Time series analysis of covariance based on linear transfer function models," Statistical Inference for Stochastic Processes, Springer, vol. 22(1), pages 1-16, April.
    2. Idris, Zera Zuryana & Ismail, Normaz Wana & Ibrahim, Saifuzzaman & Hamzah, Hanny Zurina, 2021. "High-Technology Trade: Does it Enhance National Competitiveness?," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 55(3), pages 35-48.

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