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An efficient and simple simulation smoother for state space time series analysis

Author

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  • J. Durbin and S.J. Koopman

Abstract

A simulation smoother in state space time series analysis is a procedure for drawing samples from the conditional distribution of state or disturbance vectors given the observations. We present a new technique for this which is both simple and computationally efficient.

Suggested Citation

  • J. Durbin and S.J. Koopman, 2001. "An efficient and simple simulation smoother for state space time series analysis," Computing in Economics and Finance 2001 52, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:52
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    Citations

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    Cited by:

    1. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    2. Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 29, pages 107-142, November.
    3. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2004. "Characterising the Business Cycle for Accession Countries," Working Papers 261, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. Guglielmo Maria Caporale & Abdurrahman Nazif Catik & Gül Serife Huyugüzel Kisla & Mohamad Husam Helmi & Coskun Akdeniz, 2021. "Oil Prices, Exchange Rates and Sectoral Stock Returns in the BRICS-T Countries: A Time-Varying Approach," CESifo Working Paper Series 9322, CESifo.
    5. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2019. "Money, Credit, Monetary Policy, and the Business Cycle in the Euro Area: What Has Changed Since the Crisis?," International Journal of Central Banking, International Journal of Central Banking, vol. 15(5), pages 137-173, December.
    6. C. Glocker & G. Sestieri & P. Towbin, 2017. "Time-varying fiscal spending multipliers in the UK," Working papers 643, Banque de France.
    7. Nazif Çatık, Abdurrahman & Huyugüzel Kışla, Gül & Akdeni̇z, Coşkun, 2020. "Time-varying impact of oil prices on sectoral stock returns: Evidence from Turkey," Resources Policy, Elsevier, vol. 69(C).

    More about this item

    Keywords

    State Space; Kalman filter; Gibbs sampler;
    All these keywords.

    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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