Time-Series Methods for Forecasting and Modeling Uncertainty in the Food Price Outlook

This technical bulletin describes a time-series-based approach for forecasting food prices that includes prediction intervals to communicate uncertainty. The performance of forecasts created with this approach was compared to that of previously published USDA, Economic Research Service (ERS) Food Price Outlook (FPO) forecast ranges. The methods in this new approach are intended to be used in FPO data releases that provide monthly forecasts of annual food price changes and may also prove useful in other forecasting endeavors. The new approach used an autoregressive integrated moving average (ARIMA) model that was selected based on performance (information loss), generating a more accurate forecast than previously used methods as measured by root-mean-square errors. With the parameter estimates and estimated error distribution from the optimal ARIMA model, Monte Carlo simulations are used to develop prediction intervals, which reflect uncertainty about future food prices. These prediction intervals more often included the actual annual price changes than the archived fore-cast ranges. On average, the prediction intervals also included the actual annual price change earlier in the forecasting process. These properties generally held whether we used a higher (95 percent) or lower (90 percent) confidence level. The use of standardized econometric models and model selection also allowed for the inclusion of data not currently included in FPO. The methods easily tested whether including external variables improved forecast accuracy or could be used to create new forecasts. This report considered new price change forecasts of apples, seafood, and limited-service restaurants in 2020 and the potential forecast performance improvement from incorporating futures prices as case studies.