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Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods

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  • Sanders, Dwight R.
  • Manfredo, Mark R.

Abstract

A battery of time series methods are compared for forecasting basis levels in the soybean futures complex: soybeans, soybean meal, and soybean oil. Specifically, nearby basis forecasts are generated with exponential smoothing techniques, autoregression moving average (ARMA), and vector autoregression (VAR) models. The forecasts are compared to those of the 5-year average, year ago, and no change methods. Using the 5-year average as the benchmark method, the forecast evaluation results suggest that alternative naive techniques may produce better forecasts, and the improvement gained by time series modeling is relatively small. In this sample, there is little evidence that the basis has become systematically more difficult to forecast in recent years.

Suggested Citation

  • Sanders, Dwight R. & Manfredo, Mark R., 2006. "Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(3), pages 1-11, December.
    • Handle: RePEc:ags:joaaec:43790
    DOI: 10.22004/ag.econ.43790
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    Cited by:

    1. Song, Wenxing & Fortenbery, T. Randall, 2017. "Forecasting Hard Red Winter and Soft White Wheat Basis in Washington State," 2017 Conference, April 24-25, 2017, St. Louis, Missouri 285875, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    2. Onel, Gulcan & Karali, Berna, 2014. "Relative Performance of Semi-Parametric Nonlinear Models in Forecasting Basis," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169795, Agricultural and Applied Economics Association.
    3. Bullock, David W. & Wilson, William W., "undated". "Factors Influencing the Gulf and Pacific Northwest (PNW) Soybean Export Basis: An Exploratory Statistical Analysis," Agribusiness & Applied Economics Report 288512, North Dakota State University, Department of Agribusiness and Applied Economics.
    4. Mingming Liu & Dongmei Li, 2010. "An Analysis on Total Factor Productivity and Influencing Factors of Soybean in China," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 2(2), pages 158-158, May.
    5. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    6. Ding, Kexin & Karali, Berna, 2019. "Basis Forecasting Performance of Composite Models: An Application to Corn and Soybean Markets," 2019 Conference, April 15-16, 2019, Minneapolis, Minnesota 309625, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    7. Karen E. Lewis & Ira J. Altman & Mark R. Manfredo & Dwight R. Sanders, 2015. "Risk Premiums and Forward Basis: Evidence from the Soybean Oil Market," Agribusiness, John Wiley & Sons, Ltd., vol. 31(3), pages 388-398, June.
    8. 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.
    9. Lee, Yoonsuk & Brorsen, B. Wade, 2012. "Impacts of Permanent and Transitory Shocks on Optimal Length of Moving Average to Predict Wheat Basis," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 125001, Agricultural and Applied Economics Association.
    10. Welch, J. Mark & Mkrtchyan, Vardan & Power, Gabriel J., 2009. "Predicting the Corn Basis in the Texas Triangle Area," Journal of Agribusiness, Agricultural Economics Association of Georgia, vol. 27(01-2), pages 1-15.
    11. Sanders, Daniel J. & Baker, Timothy G., 2012. "Forecasting Corn and Soybean Basis Using Regime-Switching Models," 2012 Conference, April 16-17, 2012, St. Louis, Missouri 285765, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    12. Hatchett, Robert B. & Brorsen, B. Wade & Anderson, Kim B., 2010. "Optimal Length of Moving Average to Forecast Futures Basis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 35(01), pages 1-16.
    13. Bullock, David W. & Wilson, William W. & Lakkakula, Prithviraj, 2020. "Short-Term Dynamics and Structural Changes in the United States and Brazil Soybean Basis: Seasonality, Volatility, Structural Breaks and Information Flows," 2020 Conference, St. Louis, Missouri 309641, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    14. Yoonsuk Lee & B. Wade Brorsen, 2017. "Permanent shocks and forecasting with moving averages," Applied Economics, Taylor & Francis Journals, vol. 49(12), pages 1213-1225, March.
    15. Payne, Nicholas & Karali, Berna, 2015. "Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach," 2015 Conference, April 20-21, 2015, St. Louis, Missouri 285826, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    16. Bullock, Shaina S. & Bullock, David W. & Wilson, William W., 2023. "Short-Term Factors Influencing Corn Export Basis Values in the Pre- and Post-COVID Periods: A Comparison of Econometric and Machine Learning Approaches," 2023 Conference, April 24-25, 2023, St. Louis, Missouri 379019, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

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