IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v32y2020i2p345-366.html
   My bibliography  Save this article

Stationarity test based on density approach

Author

Listed:
  • Ji Eun Moon
  • Cheolyong Park
  • Jeongcheol Ha
  • Sun Young Hwang
  • Tae Yoon Kim

Abstract

It is well known that a neighbourhood problem exists between stationarity and random walk with correlated error for any finite sample size n. That is, any stationary process is approximated by random walk with correlated error for any finite n. Hence, one cannot distinguish between them easily. In this article, we propose a stationarity test based on nonparametric density that resolves the neighbourhood problem successfully. Our stationarity test also emerges as a successful long-range dependence (LRD) stationarity test. Note that there is a similar neighbourhood problem between LRD stationarity and LRD non-stationarity [Samorodnitsky, G. (2006), ‘Long Range Dependence’, Foundations and Trends in Stochastic Systems, 1, 163–257].

Suggested Citation

  • Ji Eun Moon & Cheolyong Park & Jeongcheol Ha & Sun Young Hwang & Tae Yoon Kim, 2020. "Stationarity test based on density approach," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(2), pages 345-366, April.
    • Handle: RePEc:taf:gnstxx:v:32:y:2020:i:2:p:345-366
    DOI: 10.1080/10485252.2020.1748624
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485252.2020.1748624
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485252.2020.1748624?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:gnstxx:v:32:y:2020:i:2:p:345-366. 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/GNST20 .

    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.