IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v27y2006i4p599-612.html
   My bibliography  Save this article

Partial autocorrelation parameterization for subset autoregression

Author

Listed:
  • A. I. McLeod
  • Y. Zhang

Abstract

. A new version of the partial autocorrelation plot and a new family of subset autoregressive models are introduced. A comprehensive approach to model identification, estimation and diagnostic checking is developed for these models. These models are better suited to efficient model building of high‐order autoregressions with long time series. Several illustrative examples are given.

Suggested Citation

  • A. I. McLeod & Y. Zhang, 2006. "Partial autocorrelation parameterization for subset autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(4), pages 599-612, July.
    • Handle: RePEc:bla:jtsera:v:27:y:2006:i:4:p:599-612
    DOI: 10.1111/j.1467-9892.2006.00481.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9892.2006.00481.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9892.2006.00481.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. Zhihao Xu & Clifford M. Hurvich, 2021. "A Unified Frequency Domain Cross-Validatory Approach to HAC Standard Error Estimation," Papers 2108.06093, arXiv.org, revised Jun 2023.
    2. McLeod, A. Ian & Zhang, Ying, 2008. "Improved Subset Autoregression: With R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i02).
    3. Víctor Leiva & Helton Saulo & Rubens Souza & Robert G. Aykroyd & Roberto Vila, 2021. "A new BISARMA time series model for forecasting mortality using weather and particulate matter data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 346-364, March.
    4. Sigrunn H. Sørbye & Pedro G. Nicolau & Håvard Rue, 2022. "Finite-sample properties of estimators for first and second order autoregressive processes," Statistical Inference for Stochastic Processes, Springer, vol. 25(3), pages 577-598, October.
    5. Daniel F. Schmidt & Enes Makalic, 2013. "Estimation of stationary autoregressive models with the Bayesian LASSO," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 517-531, September.

    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:27:y:2006:i:4:p:599-612. 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.