IDEAS home Printed from https://ideas.repec.org/a/cup/jagaec/v20y1988i02p73-80_01.html
   My bibliography  Save this article

Forecasting Agricultural Prices Using a Bayesian Composite Approach

Author

Listed:
  • McIntosh, Christopher S.
  • Bessler, David A.

Abstract

Forecast users and market analysts need quality forecast information to improve their decision-making abilities. When more than one forecast is available, the analyst can improve forecast accuracy by using a composite forecast. One of several approaches to forming composite forecasts is a Bayesian approach using matrix beta priors. This paper explains the matrix beta approach and applies it to three individual forecasts of U.S. hog prices. The Bayesian composite forecast is evaluated relative to composites made from simple averages, restricted least squares, and an adaptive weighting technique.

Suggested Citation

  • McIntosh, Christopher S. & Bessler, David A., 1988. "Forecasting Agricultural Prices Using a Bayesian Composite Approach," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 20(2), pages 73-80, December.
    • Handle: RePEc:cup:jagaec:v:20:y:1988:i:02:p:73-80_01
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0081305200017611/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bessler, David A & Chamberlain, Peter J, 1987. "On Bayesian composite forecasting," Omega, Elsevier, vol. 15(1), pages 43-48.
    2. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    2. Xiaojie Xu, 2017. "Short-run price forecast performance of individual and composite models for 496 corn cash markets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2593-2620, October.
    3. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2009. "Do Composite Procedures Really Improve the Accuracy of Outlook Forecasts?," 2009 Conference, April 20-21, 2009, St. Louis, Missouri 53052, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    4. Tronstad, Russell, 1991. "The Effects Of Firm Size And Production Cost Levels On Dynamically Optimal After-Tax Cotton Storage And Hedging Decisions," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 23(1), pages 1-15, July.
    5. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip & Etienne, Xiaoli, 2012. "Composite and Outlook Forecast Accuracy," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-19, August.
    6. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    7. Xiaojie Xu & Yun Zhang, 2022. "Forecasting the total market value of a shares traded in the Shenzhen stock exchange via the neural network," Economics Bulletin, AccessEcon, vol. 42(3), pages 1266-1279.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Tae-Hwy & Yang, Weiping, 2014. "Granger-causality in quantiles between financial markets: Using copula approach," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 70-78.
    2. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    3. Goodwin, Barry K., 1992. "Forecasting Cattle Prices in the Presence of Structural Change," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 24(2), pages 11-22, December.
    4. Kyriazakou, Eleni & Panagiotidis, Theodore, 2017. "Causality analysis of the Canadian city house price indices: A cross-sample validation approach," The Journal of Economic Asymmetries, Elsevier, vol. 16(C), pages 42-52.
    5. Covey, Ted & Bessler, David A., 1991. "The Role of Futures in Daily Forward Pricing," 1991 Annual Meeting, August 4-7, Manhattan, Kansas 271282, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Henry Bryant & Michael Haigh, 2004. "Bid-ask spreads in commodity futures markets," Applied Financial Economics, Taylor & Francis Journals, vol. 14(13), pages 923-936.
    7. Fridriksson, Kari S. & Zoega, Gylfi, 2012. "Advertising as a predictor of investment," Economics Letters, Elsevier, vol. 116(1), pages 60-66.
    8. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
    9. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    10. Pär Stockhammar & Pär Österholm, 2018. "Do inflation expectations granger cause inflation?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(2), pages 403-431, August.
    11. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2016. "Stock return predictability and determinants of predictability and profits," Emerging Markets Review, Elsevier, vol. 26(C), pages 153-173.
    12. Palm, F.C., 1981. "Structural econometric modelling and time series analysis towards an integrated approach," Serie Research Memoranda 0004, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    13. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    14. Imad Moosa & Kelly Burns, 2014. "Error correction modelling and dynamic specifications as a conduit to outperforming the random walk in exchange rate forecasting," Applied Economics, Taylor & Francis Journals, vol. 46(25), pages 3107-3118, September.
    15. Mary A. Malina & Hanne S.O. Nørreklit & Frank H. Selto, 2011. "Lessons learned: advantages and disadvantages of mixed method research," Qualitative Research in Accounting & Management, Emerald Group Publishing Limited, vol. 8(1), pages 59-71, April.
    16. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Causality and predictability in distribution: The ethanol–food price relation revisited," Energy Economics, Elsevier, vol. 42(C), pages 152-160.
    17. Kastens, Terry L. & Schroeder, Ted C., 1994. "Cattle Feeder Behavior And Feeder Cattle Placements," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(2), pages 1-12, December.
    18. Norwood, F. Bailey & Schroeder, Ted C., 2000. "Usefulness Of Placement-Weight Data In Forecasting Fed Cattle Marketings And Prices," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 32(1), pages 1-10, April.
    19. Zapata, Hector O. & Gil, Jose M., 1999. "Cointegration and causality in international agricultural economics research," Agricultural Economics, Blackwell, vol. 20(1), pages 1-9, January.
    20. Zidong An & Joao Tovar Jalles, 2020. "On the performance of US fiscal forecasts: government vs. private information," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(2), pages 367-391, June.

    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:cup:jagaec:v:20:y:1988:i:02:p:73-80_01. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/aae .

    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.