Incorporating Uncertainty Into USDA Commodity Price Forecasts

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. The intervals were not very informative, with realized SAPs regularly falling outside of the USDA-forecasted intervals — particularly those made either prior to harvest or late in the marketing year — calling into question the accuracy of the USDA methodology. We demonstrate how a density forecasting format can improve the usefulness of USDA price forecasts, and we explain how such a methodology can be implemented. We simulate 21 years of out-of-sample density-based SAP forecasts using historical data, using 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 price forecasts than both its old intervals and its current single point estimates. In particular, since the option-implied volatilities (IVs) used to generate our forward-looking and composite models anticipate future market conditions, their densities reflect and adjust dynamically to market sentiment — a feature that is impossible using backward-looking data alone. At the same time, even with efficient grain and oilseed futures markets, our composite method provides useful information regarding farm-level prices beyond that contained in IVs