مورد إلكتروني
Decomposing USDA ending stocks forecast errors
| العنوان: | Decomposing USDA ending stocks forecast errors |
|---|---|
| المصدر: | Journal of Agricultural and Resource Economics 48(2): 260-276 |
| بيانات النشر: | 2023 |
| تفاصيل مُضافة: | Goyal, Raghav; Adjemian, Michael K.; Glauber, Joseph W.; Meyer, Seth http://orcid.org/0000-0003-0284-439XTest Glauber, Joseph |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | PR IFPRI3; CRP2; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; ISI MTID; PIM CGIAR Research Program on Policies, Institutions, and Markets (PIM) 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. |
| مصطلحات الفهرس: | UNITED STATES; USA; NORTH AMERICA; AMERICAS, commodity markets; agricultural prices; surveys; stocks; forecasting; artificial intelligence; machine learning; production; agriculture; economic activities; agricultural economics; economics; gradients, extreme gradient boost tree (EGBT); gradient boosting framework; gradient boosting decision trees, Journal article, Journal article, Journal Article |
| URL: | |
| الإتاحة: | Open access content. Open access content |
| ملاحظة: | English English |
| أرقام أخرى: | DFP oai:cdm15738.contentdm.oclc.org:p15738coll5/8335 10.22004/ag.econ.320674 1345288467 |
| المصدر المساهم: | INTERNATIONAL FOOD POLICY RES INST LIBR From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1345288467 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |