IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v23y2002i4p459-471.html
   My bibliography  Save this article

Nonlinear modelling of periodic threshold autoregressions using Tsmars

Author

Listed:
  • PETER A. W. LEWIS
  • BONNIE K. RAY

Abstract

We present new methods for modelling nonlinear threshold‐type autoregressive behaviour in periodically correlated time series. The methods are illustrated using a series of average monthly flows of the Fraser River in British Columbia. Commonly used nonlinearity tests of the river flow data in each month indicate nonlinear behaviour in certain months. The periodic nonlinear correlation structure is modelled nonparametrically using TSMARS, a time series version of Friedman's extended multivariate adaptive regression splines (MARS) algorithm, which allows for categorical predictor variables. We discuss two methods of using the computational algorithm in TSMARS for modelling and fitting periodically correlated data. The first method applies the algorithm to data from each period separately. The second method models data from all periods simultaneously by incorporating an additional predictor variable to distinguish different behaviour in different periods, and allows for coalescing of data from periods with similar behaviour. The models obtained using TSMARS provide better short‐term forecasts for the Fraser River data than a corresponding linear periodic AR model.

Suggested Citation

  • Peter A. W. Lewis & Bonnie K. Ray, 2002. "Nonlinear modelling of periodic threshold autoregressions using Tsmars," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 459-471, July.
    • Handle: RePEc:bla:jtsera:v:23:y:2002:i:4:p:459-471
    DOI: 10.1111/1467-9892.00269
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9892.00269
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9892.00269?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ginger M. Davis & Katherine B. Ensor, 2007. "Multivariate Time‐Series Analysis With Categorical and Continuous Variables in an Lstr Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 867-885, November.
    2. Abdelouahab Bibi & Abdelhakim Aknouche, 2010. "Yule–Walker type estimators in periodic bilinear models: strong consistency and asymptotic normality," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(1), pages 1-30, March.
    3. Tze Leung Lai & Samuel Po-Shing Wong, 2007. "Combining domain knowledge and statistical models in time series analysis," Papers math/0702814, arXiv.org.

    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:23:y:2002:i:4:p:459-471. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.