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Iterative And Recursive Estimation Of Transfer Functions

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  • Carlo Grillenzoni

Abstract

. A unified treatment of non‐linear estimation, pseudolinear regression and stochastic approximation for open‐loop transfer function models is provided. Pseudolinear regression techniques are used to derive the recursive non‐linear least‐squares estimator, avoiding the methodological problems implicit in traditional derivations. Stochastic approximation analysis is used to investigate in a direct manner the conditions of convergence and consistency of both iterative and recursive algorithms. The various methods are compared using data for an industrial process.

Suggested Citation

  • Carlo Grillenzoni, 1991. "Iterative And Recursive Estimation Of Transfer Functions," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(2), pages 105-127, March.
    • Handle: RePEc:bla:jtsera:v:12:y:1991:i:2:p:105-127
    DOI: 10.1111/j.1467-9892.1991.tb00072.x
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    Cited by:

    1. Carlo Grillenzoni, 2009. "Kernel Likelihood Inference for Time Series," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 127-140, March.
    2. Grillenzoni, Carlo, 1996. "Testing for causality in real time," Journal of Econometrics, Elsevier, vol. 73(2), pages 355-376, August.
    3. Grillenzoni, Carlo, 1998. "Forecasting unstable and nonstationary time series," International Journal of Forecasting, Elsevier, vol. 14(4), pages 469-482, December.

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