IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v4y2005i4p253-278.html
   My bibliography  Save this article

Efficient strategies for deriving the subset VAR models

Author

Listed:
  • Cristian Gatu
  • Erricos Kontoghiorghes

Abstract

Algorithms for computing the subset Vector Autoregressive (VAR) models are proposed. These algorithms can be used to choose a subset of the most statistically-significant variables of a VAR model. In such cases, the selection criteria are based on the residual sum of squares or the estimated residual covariance matrix. The VAR model with zero coefficient restrictions is formulated as a Seemingly Unrelated Regressions (SUR) model. Furthermore, the SUR model is transformed into one of smaller size, where the exogenous matrices comprise columns of a triangular matrix. Efficient algorithms which exploit the common columns of the exogenous matrices, sparse structure of the variance-covariance of the disturbances and special properties of the SUR models are investigated. The main computational tool of the selection strategies is the generalized QR decomposition and its modification. Copyright Springer-Verlag Berlin/Heidelberg 2005

Suggested Citation

  • Cristian Gatu & Erricos Kontoghiorghes, 2005. "Efficient strategies for deriving the subset VAR models," Computational Management Science, Springer, vol. 4(4), pages 253-278, November.
    • Handle: RePEc:spr:comgts:v:4:y:2005:i:4:p:253-278
    DOI: 10.1007/s10287-004-0021-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10287-004-0021-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10287-004-0021-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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Gatu, Cristian & Yanev, Petko I. & Kontoghiorghes, Erricos J., 2007. "A graph approach to generate all possible regression submodels," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 799-815, October.
    2. Hofmann, Marc & Gatu, Cristian & Kontoghiorghes, Erricos John, 2007. "Efficient algorithms for computing the best subset regression models for large-scale problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 16-29, September.
    3. Pacheco, Joaquin & Casado, Silvia & Nunez, Laura & Gomez, Olga, 2006. "Analysis of new variable selection methods for discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1463-1478, December.
    4. Gatu, Cristian & Kontoghiorghes, Erricos J., 2006. "Estimating all possible SUR models with permuted exogenous data matrices derived from a VAR process," Journal of Economic Dynamics and Control, Elsevier, vol. 30(5), pages 721-739, May.
    5. Jian Zhang, 2018. "Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-24, March.
    6. Pacheco, Joaquín & Casado, Silvia & Núñez, Laura, 2009. "A variable selection method based on Tabu search for logistic regression models," European Journal of Operational Research, Elsevier, vol. 199(2), pages 506-511, December.
    7. Gatu, Cristian & Kontoghiorghes, Erricos J. & Gilli, Manfred & Winker, Peter, 2008. "An efficient branch-and-bound strategy for subset vector autoregressive model selection," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1949-1963, June.

    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:spr:comgts:v:4:y:2005:i:4:p:253-278. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.