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Quantile Regression Methods of Estimating Confidence Intervals for WASDE Price Forecasts

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

Abstract

This paper explores the use of quantile regression for estimation of empirical confidence limits for WASDE forecasts of corn, soybean, and wheat prices. Quantile regressions for corn, soybean, and wheat forecast errors over 1980/81 through 2006/07 were specified as a function of forecast lead time. Estimated coefficients were used to calculate forecast intervals for 2007/08. The quantile regression approach to calculating forecast intervals was evaluated based on out-of-sample performance. The accuracy of the empirical confidence intervals was not statistically different from the target level about 87% of the time prior to harvest and 91% of the time after harvest.

Suggested Citation

  • Isengildina-Massa, Olga & Irwin, Scott H. & Good, Darrel L., 2008. "Quantile Regression Methods of Estimating Confidence Intervals for WASDE Price Forecasts," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6409, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea08:6409
    DOI: 10.22004/ag.econ.6409
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    References listed on IDEAS

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    1. Vogel, Fred A. & Bange, Gerald A., 1999. "Understanding USDA Crop Forecasts," USDA Miscellaneous 320799, United States Department of Agriculture.
    2. Tyrus R. Timm, 1966. "Proposals for Improvement of the Agricultural Outlook Program of the United States," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 48(5), pages 1179-1184.
    3. David M. Prescott & Thanasis Stengos, 1987. "Bootstrapping Confidence Intervals: An Application to Forecasting the Supply of Pork," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(2), pages 266-273.
    4. Williams,Jeffrey C. & Wright,Brian D., 2005. "Storage and Commodity Markets," Cambridge Books, Cambridge University Press, number 9780521023399.
    5. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    6. Westcott, Paul C. & Hoffman, Linwood A., 1999. "Price Determination for Corn and Wheat: The Role of Market Factors and Government Programs," Technical Bulletins 33581, United States Department of Agriculture, Economic Research Service.
    7. 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.
    8. Sanders, Dwight R. & Manfredo, Mark R., 2003. "USDA Livestock Price Forecasts: A Comprehensive Evaluation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(2), pages 1-19, August.
    9. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
    10. Shlyakhter, Alexander I. & Kammen, Daniel M. & Broido, Claire L. & Wilson, Richard, 1994. "Quantifying the credibility of energy projections from trends in past data : The US energy sector," Energy Policy, Elsevier, vol. 22(2), pages 119-130, February.
    11. Scott H. Irwin & Darrel L. Good, 2004. "Evaluation of USDA Interval Forecasts of Corn and Soybean Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 990-1004.
    12. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    13. David A. Bessler & John L. Kling, 1989. "The Forecast and Policy Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 503-506.
    14. Stanley Smith & Terry Sincich, 1988. "Stability over time in the distribution of population forecast errors," Demography, Springer;Population Association of America (PAA), vol. 25(3), pages 461-474, August.
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