@article{01bcb0e30f8c4f9cbf0b41e60ffc7e06,
title = "Incorporating Uncertainty into USDA Commodity Price Forecasts",
abstract = "From 1977 through April 2019, USDA published monthly season-average price (SAP) forecasts for key agricultural commodities in the form of intervals meant to indicate forecasters' uncertainty but without attaching a confidence level. In May 2019, USDA eliminated the intervals and began publishing a single point estimate—a value that has a very low probability of being realized. We demonstrate how a density forecasting format can improve the usefulness of USDA price forecasts and explain how such a methodology can be implemented. We simulate 21 years of out-of-sample density-based SAP forecasts using historical data, with forward-looking, backward-looking, and composite methods, and we evaluate them based on commonly-accepted criteria. Each of these approaches would offer USDA the ability to portray richer and more accurate price forecasts than its old intervals or its current single point estimates. Backward-looking methods require little data and provide significant improvements. For commodities with active derivatives markets, option-implied volatilities (IVs) can be used to generate forward-looking and composite models that reflect (and adjust dynamically to) market sentiment about uncertainty—a feature that is not possible using backward-looking data alone. At certain forecast steps, a composite method that combines forward- and backward-looking information provides useful information regarding farm-level prices beyond that contained in IVs.",
keywords = "USDA, WASDE, derivatives markets, forecasting, grains, option-implied volatility, situation and outlook",
author = "Adjemian, {Michael K.} and Bruno, {Valentina G.} and Robe, {Michel A.}",
note = "Funding Information: This material is based upon work supported by Cooperative Agreement #58-30000-5-0038, between the USDA Economic Research Service and American University. The authors thank two anonymous reviewers and editor Timothy Richards for their helpful comments and suggestions. While this paper was written, Adjemian and Robe were also part-time consulting economists at the U.S. Commodities Futures Trading Commission (CFTC); no CFTC resources or data were used for this project. Robe gratefully acknowledges the financial support received in his capacity as The Clearing Corporation Foundation Professor in Derivatives Trading at the University of Illinois. The views expressed in this article are those of the authors and may not be attributed to the Economic Research Service, the U.S. Department of Agriculture, the CFTC, or any other staff at those agencies. Funding Information: This material is based upon work supported by Cooperative Agreement #58‐30000‐5‐0038, between the USDA Economic Research Service and American University. The authors thank two anonymous reviewers and editor Timothy Richards for their helpful comments and suggestions. While this paper was written, Adjemian and Robe were also part‐time consulting economists at the U.S. Commodities Futures Trading Commission (CFTC); no CFTC resources or data were used for this project. Robe gratefully acknowledges the financial support received in his capacity as The Clearing Corporation Foundation Professor in Derivatives Trading at the University of Illinois. The views expressed in this article are those of the authors and may not be attributed to the Economic Research Service, the U.S. Department of Agriculture, the CFTC, or any other staff at those agencies. always σ a σ h T h Publisher Copyright: Published 2020. This article is a U.S. Government work and is in the public domain in the USA",
year = "2020",
month = mar,
day = "1",
doi = "10.1002/ajae.12075",
language = "English (US)",
volume = "102",
pages = "696--712",
journal = "American Journal of Agricultural Economics",
issn = "0002-9092",
publisher = "Oxford University Press",
number = "2",
}