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Sigma point filters for dynamic nonlinear regime switching models

Author

Listed:
  • Andrew Binning

    (Norges Bank (Central Bank of Norway))

  • Junior Maih

    (Norges Bank (Central Bank of Norway) and BI Norwegian Business School)

Abstract

In this paper we take three well known Sigma Point Filters, namely the Unscented Kalman Filter, the Divided Difference Filter, and the Cubature Kalman Filter, and extend them to allow for a very general class of dynamic nonlinear regime switching models. Using both a Monte Carlo study and real data, we investigate the properties of our proposed filters by using a regime switching DSGE model solved using nonlinear methods. We find that the proposed filters perform well. They are both fast and reasonably accurate, and as a result they will provide practitioners with a convenient alternative to Sequential Monte Carlo methods. We also investigate the concept of observability and its implications in the context of the nonlinear filters developed and propose some heuristics. Finally, we provide in the RISE toolbox, the codes implementing these three novel filters.

Suggested Citation

  • Andrew Binning & Junior Maih, 2015. "Sigma point filters for dynamic nonlinear regime switching models," Working Paper 2015/10, Norges Bank.
    • Handle: RePEc:bno:worpap:2015_10
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    Cited by:

    1. Boehl, Gregor & Strobel, Felix, 2024. "Estimation of DSGE models with the effective lower bound," Journal of Economic Dynamics and Control, Elsevier, vol. 158(C).
    2. Benchimol, Jonathan & Ivashchenko, Sergey, 2021. "Switching volatility in a nonlinear open economy," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 110, pages 1-31.
    3. Gianluca Benigno & Andrew Foerster & Christopher Otrok & Alessandro Rebucci, 2025. "Estimating macroeconomic models of financial crises: An endogenous regime‐switching approach," Quantitative Economics, Econometric Society, vol. 16(1), pages 1-47, January.
    4. Andrew Binning & Junior Maih, 2017. "Modelling Occasionally Binding Constraints Using Regime-Switching," Working Paper 2017/23, Norges Bank.
    5. Nigar Hashimzade & Oleg Kirsanov & Tatiana Kirsanova & Junior Maih, 2024. "On Bayesian Filtering for Markov Regime Switching Models," CESifo Working Paper Series 10941, CESifo.
    6. Andrew Binning & Junior Maih, 2016. "Forecast uncertainty in the neighborhood of the effective lower bound: How much asymmetry should we expect?," Working Paper 2016/13, Norges Bank.
    7. Andrew Binning & Junior Maih, 2016. "Implementing the zero lower bound in an estimated regime-switching DSGE model," Working Paper 2016/3, Norges Bank.
    8. Luisa Corrado & Stefano Grassi & Aldo Paolillo, 2021. "Modelling and Estimating Large Macroeconomic Shocks During the Pandemic," National Institute of Economic and Social Research (NIESR) Discussion Papers 530, National Institute of Economic and Social Research.
    9. Boehl, Gregor & Strobel, Felix, 2024. "The empirical performance of the financial accelerator since 2008," Journal of Economic Dynamics and Control, Elsevier, vol. 167(C).
    10. Luisa Corrado & Stefano Grassi & Aldo Paolillo, 2021. "Identifying Economic Shocks in a Rare Disaster Environment," CEIS Research Paper 517, Tor Vergata University, CEIS, revised 18 Jul 2024.
    11. Sanha Noh, 2020. "Posterior Inference on Parameters in a Nonlinear DSGE Model via Gaussian-Based Filters," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 795-841, December.
    12. Andrew Foerster & Christian Matthes, 2022. "Learning About Regime Change," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1829-1859, November.
    13. Karamé, Frédéric, 2018. "A new particle filtering approach to estimate stochastic volatility models with Markov-switching," Econometrics and Statistics, Elsevier, vol. 8(C), pages 204-230.

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