Decomposing USDA Ending Stocks Forecast Errors

Raghav Goyal, Michael K. Adjemian, Joseph Glauber and Seth Meyer

No 316407, 2021 Conference from NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management

Abstract: 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 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, Extreme Gradient Boost Tree (EGBT), that uses a gradient boosting framework and is robust to multicollinearity. Our results indicate export and production misses to be the major contributors to ending stocks projection errors. Because foreign imports are likely tied to foreign production deficits, 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 Europe cost USDA the most.

Keywords: Marketing (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/316407/files/G ... er_NCCC-134_2021.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:nccc21:316407

DOI: 10.22004/ag.econ.316407

Access Statistics for this paper

More papers in 2021 Conference from NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management
Bibliographic data for series maintained by AgEcon Search ().