IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v58y2023ipcs1544612323008978.html
   My bibliography  Save this article

A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast

Author

Listed:
  • Wang, Renhe
  • Wang, Tong
  • Qian, Zhiyong
  • Hu, Shulan

Abstract

We introduce an efficient Markov Chain Monte Carlo sampler in precision-based algorithms for the estimation of the Random Switching Exponential Smoothing model, a versatile forecasting mechanism for time series data characterized with changing trends. Through a series of simulation experiments, RC-MCMC exhibits superior parameter estimation accuracy, particularly for datasets featuring low persistence trends. Furthermore, an empirical evaluation using the Bank for International Settlements’ quarterly time series data on the non-financial sector’s total credit relative to GDP validates the findings. The out-of-sample results indicate that the proposed approach outperforms its counterparts in estimating and forecasting accuracy for trending time series data.

Suggested Citation

  • Wang, Renhe & Wang, Tong & Qian, Zhiyong & Hu, Shulan, 2023. "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008978
    DOI: 10.1016/j.frl.2023.104525
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323008978
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.104525?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008978. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.