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A note on implementing the Durbin and Koopman simulation smoother

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  • Jarocinski, Marek

Abstract

The correct implementation of the Durbin and Koopman simulation smoother is explained. A possible misunderstanding is pointed out and clarified for both the basic state space model and for its extension that allows time-varying intercepts (mean adjustments).

Suggested Citation

  • Jarocinski, Marek, 2014. "A note on implementing the Durbin and Koopman simulation smoother," MPRA Paper 59466, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:59466
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    References listed on IDEAS

    as
    1. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    2. Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, vol. 18(1), pages 49-75, July.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
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    7. Marcin Jurek & Matthias Katzfuss, 2023. "Scalable spatio‐temporal smoothing via hierarchical sparse Cholesky decomposition," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.

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    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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