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Estimability of the linear effects in state space models with an unknown initial condition

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  • Rajesh Selukar

Abstract

In the case of state space models with an unknown initial condition, the diffuse Kalman smoother can be used to obtain smoothed state estimates. When the full initial state is not estimable because the available data are insufficient, some linear combinations of the states can still be estimable. This brief note provides a simple method to determine whether a linear combination of a state is estimable.

Suggested Citation

  • Rajesh Selukar, 2010. "Estimability of the linear effects in state space models with an unknown initial condition," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(3), pages 167-168, May.
    • Handle: RePEc:bla:jtsera:v:31:y:2010:i:3:p:167-168
    DOI: 10.1111/j.1467-9892.2010.00653.x
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    References listed on IDEAS

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    1. Piet De Jong & Singfat Chu‐Chun‐Lin, 2003. "Smoothing With An Unknown Initial Condition," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 141-148, March.
    2. S. J. Koopman & J. Durbin, 2000. "Fast Filtering and Smoothing for Multivariate State Space Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(3), pages 281-296, May.
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