IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v10y1989i3p271-281.html
   My bibliography  Save this article

The Estimation Of The Order Of An Autoregression Using Recursive Residuals And Cross‐Validation

Author

Listed:
  • L. Kavalieris

Abstract

. Several criteria for the estimation of the order of an autoregressive representation of a stationary time series are examined. There need not be a true finite‐order autoregression model for the data, so that the purpose of model identification is to obtain an adequate representation of the data. It is proved that minimizing the sum of squares of recursive residuals (the ‘predictive minimizing description length’) is equivalent to minimizing BIC. The equivalence between the cross‐validation and Akaike information criterion methods of autoregressive modelling is also established.

Suggested Citation

  • L. Kavalieris, 1989. "The Estimation Of The Order Of An Autoregression Using Recursive Residuals And Cross‐Validation," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 271-281, May.
    • Handle: RePEc:bla:jtsera:v:10:y:1989:i:3:p:271-281
    DOI: 10.1111/j.1467-9892.1989.tb00028.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9892.1989.tb00028.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9892.1989.tb00028.x?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. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & Georgios P. Kouretas, 2006. "Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 371-383.
    2. F. Gonzalez Miranda & N. Burgess, 1997. "Modelling market volatilities: the neural network perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 3(2), pages 137-157.
    3. Kanas, Angelos & Yannopoulos, Andreas, 2001. "Comparing linear and nonlinear forecasts for stock returns," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 383-398, December.
    4. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.

    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:10:y:1989:i:3:p:271-281. 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.