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Modeling Soybean Prices in a Changing Policy Environment

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  • Goodwin, Barry K.
  • Schnepf, Randall D.
  • Dohlman, Erik

Abstract

The oilseed products complex is an important component of the U.S. agricultural sector. In 2000, almost 75 million acres were planted to soybeans, representing over 29 percent of total planted acreage, making soybeans second only to corn in terms of acreage (ERS/USDA, 2000). Soybean acreage has increased steadily since 1990, when only 58 million acres were planted. From a historical perspective, soybeans are rather unique in that they were not eligible for target-price deficiency payments nor were they subject to the explicit acreage restrictions of other program crops. However, the acreage-idling and base-acreage requirements, as well as government stock-holding behavior, of other program crops has indirectly affected soybean acreage decisions in the past. Soybeans have been eligible for government price support loans for the past sixty years. In recent years, soybeans have benefited from a high loan rate relative to corn. This, coupled with eligibility for government marketing loan gains and loan deficiency payments, has stimulated production of soybeans.

Suggested Citation

  • Goodwin, Barry K. & Schnepf, Randall D. & Dohlman, Erik, 2001. "Modeling Soybean Prices in a Changing Policy Environment," 2001 Conference, April 23-24, 2001, St. Louis, Missouri 18946, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    • Handle: RePEc:ags:ncrone:18946
    DOI: 10.22004/ag.econ.18946
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    References listed on IDEAS

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    1. Westcott, Paul C. & Price, J. Michael, 2001. "Analysis Of The U.S. Commodity Loan Program With Marketing Loan Provisions," Agricultural Economic Reports 34035, United States Department of Agriculture, Economic Research Service.
    2. Tsurumi, Hiroki & Wago, Hajime & Ilmakunnas, Pekka, 1986. "Gradual switching multivariate regression models with stochastic cross-equational constraints and an application to the Klem translog production model," Journal of Econometrics, Elsevier, vol. 31(3), pages 235-253, April.
    3. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
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    1. No, Sung Chul & Salassi, Michael E., 2006. "Dynamic Analysis and Forecasts of Rough Rice Price under Government Price Support Program: An Application of Bayesian VAR," 2006 Annual Meeting, February 5-8, 2006, Orlando, Florida 35279, Southern Agricultural Economics Association.
    2. David Ubilava, 2012. "Modeling Nonlinearities in the U.S. Soybean‐to‐Corn Price Ratio: A Smooth Transition Autoregression Approach," Agribusiness, John Wiley & Sons, Ltd., vol. 28(1), pages 29-41, January.
    3. Flanders, Archie, 2017. "Equilibrium Analysis of Stocks-to-Use and Price for Long-Grain Rice," Journal of the ASFMRA, American Society of Farm Managers and Rural Appraisers, vol. 2017.
    4. Dinggao Liu & Zhenpeng Tang & Yi Cai, 2022. "A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    5. Aliaga Lordemann, Javier & Mora-García, Claudio & Mulder, Nanno, 2021. "The main drivers of arabica coffee prices in Latin America," Documentos de Proyectos 46729, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    6. John B. Mitchell, 2010. "Soybean Futures Crush Spread Arbitrage: Trading Strategies and Market Efficiency," JRFM, MDPI, vol. 3(1), pages 1-34, December.
    7. Isengildina-Massa, Olga & MacDonald, Stephen, 2009. "U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49324, Agricultural and Applied Economics Association.

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