Decomposing USDA Ending Stocks Forecast Errors

JOURNAL OF AGRICULTURAL AND RESOURCE ECONOMICS(2023)

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摘要
The USDA publishes monthly ending stocks projections, providing an estimate of the end -of-marketing-year inventory of a particular commodity. By comparing these projections of balance-sheet variables against their realized values from marketing years 1992/3 to 2019/20, we decompose ending stocks forecast errors into errors of the other supply and demand components. Our results indicate that export and production misses are the key contributors to projection errors. We likewise investigate US export errors. Our results make a strong case that better information about production expectations, both domestically and worldwide, will contribute to more efficient agricultural balance-sheet forecasts.
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关键词
corn, cotton, gradient boosting trees, machine learning, SHAP decomposition, soybeans, wheat
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