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Information Rigidity and Correcting Inefficiency in USDA’s Commodity Forecasts

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  • MacDonald, Stephen
  • Isengildina-Massa, Olga

Abstract

This study investigates the rationality of monthly revisions in annual forecasts of supply, demand and price for U.S. corn, cotton, soybeans, and wheat, published in the World Agricultural Supply and Demand Estimates over 1985/86-2010/11. The findings indicate that USDA's forecast revisions are not independent across months, and that forecasts are typically smoothed. Adjustment for smoothing in a subset of forecasts (1998/2000-2010/11) showed mixed results: significant improvements for soybean use forecasts, cotton exports, and a broad cross-section of forecasts published in October. However, accuracy deteriorated in some cases, particularly for late-season preliminary data revisions.

Suggested Citation

  • MacDonald, Stephen & Isengildina-Massa, Olga, 2012. "Information Rigidity and Correcting Inefficiency in USDA’s Commodity Forecasts," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124890, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea12:124890
    DOI: 10.22004/ag.econ.124890
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    References listed on IDEAS

    as
    1. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    2. Olga Isengildina & Scott H. Irwin & Darrel L. Good, 2006. "Are Revisions to USDA Crop Production Forecasts Smoothed?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(4), pages 1091-1104.
    3. repec:ags:jrapmc:122314 is not listed on IDEAS
    4. Isengildina-Massa, Olga & MacDonald, Stephen & Xie, Ran, 2012. "A Comprehensive Evaluation of USDA Cotton Forecasts," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(1), pages 1-16, April.
    Full references (including those not matched with items on IDEAS)

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    Keywords

    Crop Production/Industries; Research Methods/ Statistical Methods;

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