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Filtering and smoothing of state vector for diffuse state‐space models

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  • S. J. Koopman
  • J. Durbin

Abstract

. This paper presents exact recursions for calculating the mean and mean square error matrix of the state vector given the observations for the multi‐variate linear Gaussian state‐space model in the case where the initial state vector is (partially) diffuse.

Suggested Citation

  • S. J. Koopman & J. Durbin, 2003. "Filtering and smoothing of state vector for diffuse state‐space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 85-98, January.
    • Handle: RePEc:bla:jtsera:v:24:y:2003:i:1:p:85-98
    DOI: 10.1111/1467-9892.00294
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    References listed on IDEAS

    as
    1. William Bell & Steven Hillmer, 1991. "Initializing The Kalman Filter For Nonstationary Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(4), pages 283-300, July.
    2. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    3. Ralph D. Snyder & Grant R. Saligari, 1996. "Initialization Of The Kalman Filter With Partially Diffuse Initial Conditions," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(4), pages 409-424, July.
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