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Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach

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  • Payne, Nicholas D.
  • Karali, Berna
  • Dorfman, Jeffrey H.

Abstract

We use experimental methods to investigate subsidy incidence, the transfer of subsidy payments from intended recipients to other economic agents, in privately negotiated spot markets. Our results show that market outcomes in treatments with a subsidy given to either buyers or sellers are significantly different from both a no-subsidy treatment and the competitive prediction of a 50% subsidy incidence. The disparity in incidence across treatments relative to predicted levels suggests that incidence equivalence does not hold in this market setting. Moreover, we find no statistical difference in market outcomes when benefits are framed as a “subsidy” versus a schedule shift.

Suggested Citation

  • Payne, Nicholas D. & Karali, Berna & Dorfman, Jeffrey H., 2019. "Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 51(2), February.
    • Handle: RePEc:ags:joaaec:356471
    DOI: 10.22004/ag.econ.356471
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    1. Payne, Nicholas D. & Karali, Berna & Dorfman, Jeffrey H., 2019. "Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 51(2), pages 249-266, May.
    2. Irwin, Scott H. & Garcia, Philip & Good, Darrel L. & Kunda, Eugene L., 2009. "Poor Convergence Performance of CBOT Corn, Soybean and Wheat Futures Contracts: Causes and Solutions," Marketing and Outlook Research Reports 183475, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
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    1. Payne, Nicholas D. & Karali, Berna & Dorfman, Jeffrey H., 2019. "Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 51(2), pages 249-266, May.
    2. Augusto Hauber Gameiro, 2022. "Factors Defining Prices of Finished Cattle in Mato Grosso Contrasted Within Brazil’s Pricing Structure," Journal of Agricultural Studies, Macrothink Institute, vol. 10(4), pages 218-235, December.
    3. A. Ford Ramsey & Michael K. Adjemian, 2024. "Forecast combination in agricultural economics: Past, present, and the future," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(4), pages 1450-1478, December.
    4. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.

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    Keywords

    Livestock Production/Industries;

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