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Kalman Filtering Applied to Statistical Forecasting

Author

Listed:
  • G. W. Morrison

    (Computer Sciences Division, Oak Ridge National Laboratory)

  • D. H. Pike

    (University of Tennessee)

Abstract

This paper describes the use of the Kalman Filter in a certain ciass of forecasting problems. The time series is assumed to be modeled as a time varying mean with additive noise. The mean of the time series is assumed to be a linear combination of known functions. The coefficients appearing in the linear combination are unknown. Under such assumptions, the time series can be described as a linear system with the state vector of the system being the unknown parameters and present value of the mean of the process. The Kalman Filter can be used under these circumstances to obtain an "optimal" estimate of the state vector. One of the distinct advantages of the Kalman Filter is that time varying coefficients can be permitted in the model. Examples using the Kalman Filter in forecasting are presented.

Suggested Citation

  • G. W. Morrison & D. H. Pike, 1977. "Kalman Filtering Applied to Statistical Forecasting," Management Science, INFORMS, vol. 23(7), pages 768-774, March.
    • Handle: RePEc:inm:ormnsc:v:23:y:1977:i:7:p:768-774
    DOI: 10.1287/mnsc.23.7.768
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    Cited by:

    1. Eduardo Loría & Manuel G. Ramos., 2007. "La ley de Okun: una relectura para México, 1970-2004," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 22(1), pages 19-55.
    2. Roula Inglesi-Lotz, 2012. "The sensitivity of the South African industrial sector’s electricity consumption to electricity price fluctuations," Working Papers 201225, University of Pretoria, Department of Economics.
    3. Muhammad Akram GILAL* & Muhammad AJMAIR** & Sohail FAROOQ***, 2019. "Structural Changes And Economic Growth In Pakistan," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 29(1), pages 35-51.
    4. Felix Wick & Ulrich Kerzel & Martin Hahn & Moritz Wolf & Trapti Singhal & Daniel Stemmer & Jakob Ernst & Michael Feindt, 2021. "Demand Forecasting of Individual Probability Density Functions with Machine Learning," SN Operations Research Forum, Springer, vol. 2(3), pages 1-39, September.
    5. Inglesi-Lotz, R., 2011. "The evolution of price elasticity of electricity demand in South Africa: A Kalman filter application," Energy Policy, Elsevier, vol. 39(6), pages 3690-3696, June.
    6. Jang, Woan-Yuh & Lee, Jie-Haun & Hu, Hsueh-Chin, 2016. "Halo, horn, or dark horse biases: Corporate reputation and the earnings announcement puzzle," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 272-289.
    7. Ambreen ZEB & Khadim HUSSAIN & Usman AHMAD & Muhammad AJMAIR, 2017. "Factors affecting the services sector growth in Pakistan: A time varying parametric approach," Journal of Economics Library, KSP Journals, vol. 4(3), pages 388-395, September.
    8. Masike, Kabelo & Vermeulen, Cobus, 2022. "The time-varying elasticity of South African electricity demand," Energy, Elsevier, vol. 238(PC).
    9. Yoonsuk Lee & B. Wade Brorsen, 2017. "Permanent Breaks and Temporary Shocks in a Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 49(2), pages 255-270, February.

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