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Modelling soybean prices in a changing policy environment

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  • Barry Goodwin
  • Randy Schnepf
  • Erik Dohlman

Abstract

Accurate forecasts of commodity prices are an important ingredient in the policy formation process. A commodity price forecasting procedure used routinely by the US Department of Agriculture in their policy and market analysis activities is a simple, linear, reduced-form regression model that predicts season-average farm prices (SAFP) using policy variables and the ratio of total ending stocks to use. This approach is extended to the soybean SAFP to estimate a benchmark model using annual data. Also several specification issues related to this estimation framework are addressed. Evaluation suggests that the standard forecasting procedure may be affected by the fact that the ratio of stocks to use is endogenous to prices. In addition, important structural changes are revealed in these relationships over time. A model is then considered that allows parameters to shift gradually. Improvements in the accuracy of model forecasts allowed by this parameter switching technique are identified and discussed. In addition, the exact nature of the structural shifts is evaluated using dynamic impulse response functions.

Suggested Citation

  • Barry Goodwin & Randy Schnepf & Erik Dohlman, 2005. "Modelling soybean prices in a changing policy environment," Applied Economics, Taylor & Francis Journals, vol. 37(3), pages 253-263.
    • Handle: RePEc:taf:applec:v:37:y:2005:i:3:p:253-263
    DOI: 10.1080/00036840412331315060
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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. 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.
    2. 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).
    3. John B. Mitchell, 2010. "Soybean Futures Crush Spread Arbitrage: Trading Strategies and Market Efficiency," JRFM, MDPI, vol. 3(1), pages 1-34, December.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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