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Robust estimation of linear state space models

Author

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  • Ruben Crevits
  • Christophe Croux

Abstract

The model parameters of linear state space models are typically estimated with maximum likelihood estimation, where the likelihood is computed analytically with the Kalman filter. Outliers can deteriorate the estimation. Therefore we propose an alternative estimation method. The Kalman filter is replaced by a robust version and the maximum likelihood estimator is robustified as well. The performance of the robust estimator is investigated in a simulation study. Robust estimation of time varying parameter regression models is considered as a special case. Finally, the methodology is applied to real data.

Suggested Citation

  • Ruben Crevits & Christophe Croux, 2017. "Robust estimation of linear state space models," Working Papers of Department of Decision Sciences and Information Management, Leuven 588734, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
    • Handle: RePEc:ete:kbiper:588734
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    File URL: https://lirias.kuleuven.be/retrieve/462644
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    Keywords

    Kalman Filter; Forecasting; Outliers; Time varying parameters;
    All these keywords.

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