Decomposing USDA ending stocks forecast errors
The U.S. Department of Agriculture (USDA) publishes monthly Ending Stocks projections,providing an estimate of the end-of-marketing-year inventory of a particular commodity, which effectively summarizes its supply and demand outlook. By comparing USDA’s projections of balance sheet variables against...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/140422 |
| _version_ | 1855514878192648192 |
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| author | Goyal, Raghav Adjemian, Michael K. Glauber, Joseph W. Meyer, Seth |
| author_browse | Adjemian, Michael K. Glauber, Joseph W. Goyal, Raghav Meyer, Seth |
| author_facet | Goyal, Raghav Adjemian, Michael K. Glauber, Joseph W. Meyer, Seth |
| author_sort | Goyal, Raghav |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The U.S. Department of Agriculture (USDA) publishes monthly Ending Stocks projections,providing an estimate of the end-of-marketing-year inventory of a particular commodity, which effectively summarizes its supply and demand outlook. By comparing USDA’s 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. We apply a decision-tree-based ensemble Machine Learning (ML) algorithm, the Extreme Gradient Boost Tree (EGBT), that uses a gradient boosting framework and is robust to multicollinearity. Our results indicate that export and production misses are the key contributors to ending stocks projection errors. Because foreign imports of U.S. products are likely tied to foreign production beficits, we likewise investigate how U.S. export errors are linked to USDA’s foreign production and export forecast misses, country-by-country, and show that misses on production and export levels in China, Mexico, Brazil, and European Union cost USDA the most. Overall, 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. |
| format | Journal Article |
| id | CGSpace140422 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | CGSpace1404222024-11-13T12:23:07Z Decomposing USDA ending stocks forecast errors Goyal, Raghav Adjemian, Michael K. Glauber, Joseph W. Meyer, Seth production frameworks forecasting surveys machine learning slope stocks artificial intelligence agricultural prices agriculture economics economic activities agricultural economics commodity markets The U.S. Department of Agriculture (USDA) publishes monthly Ending Stocks projections,providing an estimate of the end-of-marketing-year inventory of a particular commodity, which effectively summarizes its supply and demand outlook. By comparing USDA’s 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. We apply a decision-tree-based ensemble Machine Learning (ML) algorithm, the Extreme Gradient Boost Tree (EGBT), that uses a gradient boosting framework and is robust to multicollinearity. Our results indicate that export and production misses are the key contributors to ending stocks projection errors. Because foreign imports of U.S. products are likely tied to foreign production beficits, we likewise investigate how U.S. export errors are linked to USDA’s foreign production and export forecast misses, country-by-country, and show that misses on production and export levels in China, Mexico, Brazil, and European Union cost USDA the most. Overall, 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. 2023-05-01 2024-03-14T12:09:29Z 2024-03-14T12:09:29Z Journal Article https://hdl.handle.net/10568/140422 en Open Access Goyal, Raghav; Adjemian, Michael K.; Glauber, Joseph W.; and Meyer, Seth. 2023. Decomposing USDA ending stocks forecast errors. Journal of Agricultural and Resource Economics 48(2): 260-276. https://purl.umn.edu/320674 |
| spellingShingle | production frameworks forecasting surveys machine learning slope stocks artificial intelligence agricultural prices agriculture economics economic activities agricultural economics commodity markets Goyal, Raghav Adjemian, Michael K. Glauber, Joseph W. Meyer, Seth Decomposing USDA ending stocks forecast errors |
| title | Decomposing USDA ending stocks forecast errors |
| title_full | Decomposing USDA ending stocks forecast errors |
| title_fullStr | Decomposing USDA ending stocks forecast errors |
| title_full_unstemmed | Decomposing USDA ending stocks forecast errors |
| title_short | Decomposing USDA ending stocks forecast errors |
| title_sort | decomposing usda ending stocks forecast errors |
| topic | production frameworks forecasting surveys machine learning slope stocks artificial intelligence agricultural prices agriculture economics economic activities agricultural economics commodity markets |
| url | https://hdl.handle.net/10568/140422 |
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