IDEAS home Printed from https://ideas.repec.org/p/ags/asea26/404830.html

Systems Forecasting By-Parts: A Study on Fed Beef Production

Author

Listed:
  • Dodd-Zakely, Brandon T.
  • Koontz, Stephen R.

Abstract

This study examines forecasts of fed beef production through a by-parts estimation framework to provide possibly more practical alternatives to relatively complex systems of equations methods. Recent shifts in cattle production, including elevated contributions of heifers in the slaughter mix and larger weights of fed cattle, provide an appropriate case study that underscores the need to revisit past events in the development of new forecasting strategies. By analyzing survey history in the data selection process, examining analog time periods, and considering the potential of serial correlation within Deterministic Trend / Deterministic Seasonality models, this study highlights that practical enhancements to forecast accuracy can be sustained by simple procedures. The results of this study demonstrate that (1) historical data selection can significantly impact forecast quality; (2) correcting autocorrelated errors can improve model performance; (3) the effectiveness of different forecast estimation methods varies by the selected horizon; and (4) the use of more simplistic assumptions underlying a forecast model can produce competitive and accurate results.

Suggested Citation

  • Dodd-Zakely, Brandon T. & Koontz, Stephen R., 2026. "Systems Forecasting By-Parts: A Study on Fed Beef Production," 3rd ASEAN University Symposium for Sustainable Food Systems, Faculty of Agriculture, Chiang Mai University, Thailand, May 21-22, 2026 404830, Association of Southeast Asian Nations (ASEAN).
    • Handle: RePEc:ags:asea26:404830
    DOI: 10.22004/ag.econ.404830
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/404830/files/Stephen_Koontz_Dodd_Koontz_NCCC-134_2026.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.404830?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
    ---><---

    More about this item

    Keywords

    ;
    ;

    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:ags:asea26:404830. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aseanid.html .

    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.