Incorporating Uncertainty into USDA Commodity Price Forecasts

Michael K. Adjemian, Valentina G. Bruno, Michel A. Robe

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)696-712
Number of pages17
JournalAmerican Journal of Agricultural Economics
Volume102
Issue number2
DOIs
StatePublished - Mar 1 2020

Keywords

  • USDA
  • WASDE
  • derivatives markets
  • forecasting
  • grains
  • option-implied volatility
  • situation and outlook

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Economics and Econometrics

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