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Continuous Time State Space Modelling with an Application to High-Frequency Road Traffic Data

In: Continuous Time Modeling in the Behavioral and Related Sciences

Author

Listed:
  • Siem Jan Koopman

    (Vrije Universiteit Amsterdam, Department of Econometrics)

  • Jacques J. F. Commandeur

    (Vrije Universiteit Amsterdam, Department of Econometrics)

  • Frits D. Bijleveld

    (Vrije Universiteit Amsterdam, Department of Econometrics)

  • Sunčica Vujić

    (University of Antwerp, Department of Economics)

Abstract

We review Kalman filter and related smoothing methods for the continuous time state space model. The attractive property of continuous time state space models is that time gaps between consecutive observations in a time series are allowed to vary throughout the process. We discuss some essential details of the continuous time state space methodology and review the similarities and the differences between the continuous time and discrete time approaches. An application in the modelling of road traffic data is presented in order to illustrate the relevance of continuous time state space modelling in practice.

Suggested Citation

  • Siem Jan Koopman & Jacques J. F. Commandeur & Frits D. Bijleveld & Sunčica Vujić, 2018. "Continuous Time State Space Modelling with an Application to High-Frequency Road Traffic Data," Springer Books, in: Kees van Montfort & Johan H. L. Oud & Manuel C. Voelkle (ed.), Continuous Time Modeling in the Behavioral and Related Sciences, chapter 0, pages 305-315, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-77219-6_13
    DOI: 10.1007/978-3-319-77219-6_13
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