IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i24p8776-8785.html
   My bibliography  Save this article

Uncertain threshold autoregressive model with imprecise observations

Author

Listed:
  • Han Tang

Abstract

Uncertain time series analysis is a methodology that deals with expert’s experimental time series data. Previous studies mainly focus on linear models such as an uncertain autoregressive (UAR) model. Nevertheless, the laws of motion in the real world are usually non linear. In order to model the observations that periodically vary over time, this article introduces an uncertain threshold autoregressive (UTAR) model. Then unknown parameters in the UTAR model can be estimated with the least squares estimation and residual analysis is presented. Furthermore, we discuss the forecast value and confidence interval for variables in the next periods. Ultimately, a numerical example is given.

Suggested Citation

  • Han Tang, 2022. "Uncertain threshold autoregressive model with imprecise observations," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(24), pages 8776-8785, December.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:24:p:8776-8785
    DOI: 10.1080/03610926.2021.1906433
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1906433
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1906433?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:lstaxx:v:51:y:2022:i:24:p:8776-8785. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.