IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v39y2018i4p592-617.html
   My bibliography  Save this article

On Local Trigonometric Regression Under Dependence

Author

Listed:
  • Jan Beran
  • Britta Steffens
  • Sucharita Ghosh

Abstract

We consider nonparametric estimation of an additive time series decomposition into a long†term trend μ and a smoothly changing seasonal component S under general assumptions on the dependence structure of the residual process. The rate of convergence of local trigonometric regression estimators of S turns out to be unaffected by the dependence, even though the spectral density of the residual process has a pole at the origin. In contrast, the rate of convergence of nonparametric estimators of μ depends on the long†memory parameter d. Therefore, in the presence of long†range dependence, different bandwidths for estimating μ and S should be used. A data adaptive algorithm for optimal bandwidth choice is proposed. Simulations and data examples illustrate the results.

Suggested Citation

  • Jan Beran & Britta Steffens & Sucharita Ghosh, 2018. "On Local Trigonometric Regression Under Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(4), pages 592-617, July.
  • Handle: RePEc:bla:jtsera:v:39:y:2018:i:4:p:592-617
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jtsa.12287
    Download Restriction: no

    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:bla:jtsera:v:39:y:2018:i:4:p:592-617. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.