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Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts

Author

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  • Isengildina-Massa, Olga
  • Irwin, Scott H.
  • Good, Darrel L.

Abstract

This study uses quantile regressions to estimate historical forecast error distributions for WASDE forecasts of corn, soybean, and wheat prices, and then compute confidence limits for the forecasts based on the empirical distributions. Quantile regressions with fit errors expressed as a function of forecast lead time are consistent with theoretical forecast variance expressions while avoiding assumptions of normality and optimality. Based on out-of-sample accuracy tests over 1995/96–2006/07, quantile regression methods produced intervals consistent with the target confidence level. Overall, this study demonstrates that empirical approaches may be used to construct accurate confidence intervals for WASDE corn, soybean, and wheat price forecasts.

Suggested Citation

  • Isengildina-Massa, Olga & Irwin, Scott H. & Good, Darrel L., 2010. "Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 35(3), pages 1-23, December.
    • Handle: RePEc:ags:jlaare:99120
    DOI: 10.22004/ag.econ.99120
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    File URL: https://ageconsearch.umn.edu/record/99120/files/JARE_Dec2010__12F_pp545-567.pdf
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    References listed on IDEAS

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    1. Hautsch, Nikolaus & Hess, Dieter, 2007. "Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(1), pages 189-208, March.
    2. Taylor, James W. & Bunn, Derek W., 1999. "Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: A simulation study," International Journal of Forecasting, Elsevier, vol. 15(3), pages 325-339, July.
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    Cited by:

    1. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    2. Chavas, Jean-Paul & Li, Jian & Wang, Linjie, 2024. "Option pricing revisited: The role of price volatility and dynamics," Journal of Commodity Markets, Elsevier, vol. 33(C).
    3. Sun, Zhining & Katchova, Ani, 2024. "Herding in the WASDE," 2024 Annual Meeting, July 28-30, New Orleans, LA 344064, Agricultural and Applied Economics Association.
    4. Chavas, Jean-Paul & Li, Jian & Wang, Linjie, 2024. "Option Pricing Revisited: The Role of Price Volatility and Dynamics," 2024 Annual Meeting, July 28-30, New Orleans, LA 343544, Agricultural and Applied Economics Association.
    5. Etienne, Xiaoli L. & Farhangdoost, Sara & Hoffman, Linwood A. & Adam, Brian D., 2023. "Forecasting the U.S. season-average farm price of corn: Derivation of an alternative futures-based forecasting model," Journal of Commodity Markets, Elsevier, vol. 30(C).
    6. Adjemian, Michael K. & Bruno, Valentina G. & Robe, Michel A., 2016. "Forward‐Looking USDA Price Forecasts," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235931, Agricultural and Applied Economics Association.

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