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Gauss, Kalman and advances in recursive parameter estimation

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  • Peter C. Young

Abstract

The paper considers how the Kalman filter has influenced the development of recursive parameter estimation since the publication of Rudolf Kalman's seminal article in 1960. It will present a partial review of developments over the past half century and provide a tutorial introduction to the refined instrumental variable approach to the optimal recursive estimation of parameters in both discrete and continuous-time transfer function models. The paper concludes with a case study that shows how recursive parameter estimation and the Kalman filter can be combined in the design and development of a real‐time adaptive forecasting and data assimilation system for flow in river systems. Copyright (C) 2010 John Wiley & Sons, Ltd.

Suggested Citation

  • Peter C. Young, 2011. "Gauss, Kalman and advances in recursive parameter estimation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(1), pages 104-146, January.
    • Handle: RePEc:jof:jforec:v:30:y:2011:i:1:p:104-146
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    File URL: http://hdl.handle.net/10.1002/for.1187
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    Cited by:

    1. Abdelhamid Ouakasse & Guy Mélard, 2017. "A New Recursive Estimation Method for Single Input Single Output Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 417-457, May.
    2. Terence C. Mills, 2013. "Trends, cycles and structural breaks," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 3, pages 45-60, Edward Elgar Publishing.

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