IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v26y2022i1p25-34n9.html
   My bibliography  Save this article

Bayesian inference for unit root in smooth transition autoregressive models and its application to OECD countries

Author

Listed:
  • Jaiswal Shivam
  • Chaturvedi Anoop

    (Department of Statistics, University of Allahabad, Allahabad, India)

  • Bhatti Muhammad Ishaq

    (Department of Economics, Finance & Marketing, LBS, La Trobe University, Melbourne, Australia)

Abstract

This paper proposes a Bayesian unit root test for testing a non-stationary random walk of nonlinear exponential smooth transition autoregressive process. It investigates the performance of Bayes estimators and Bayesian unit root test due to its superiority in estimation and power properties than reported in existing literature. The proposed approach is applied to the real effective exchange rates of 10 selected countries of the organization of economic co-operation and development (OECD) and the paper observe some interesting findings which demonstrate the usefulness of the model.

Suggested Citation

  • Jaiswal Shivam & Chaturvedi Anoop & Bhatti Muhammad Ishaq, 2022. "Bayesian inference for unit root in smooth transition autoregressive models and its application to OECD countries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(1), pages 25-34, February.
  • Handle: RePEc:bpj:sndecm:v:26:y:2022:i:1:p:25-34:n:9
    DOI: 10.1515/snde-2019-0133
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/snde-2019-0133
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/snde-2019-0133?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:bpj:sndecm:v:26:y:2022:i:1:p:25-34:n:9. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.