Adaptive food price forecasting improves public information in times of rapid economic change

Matthew J. MacLachlan (), Michael K. Adjemian, Xiaoli Etienne, Megan Sweitzer, Richard Volpe and Wendy Zeng
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Matthew J. MacLachlan: Cornell University
Michael K. Adjemian: University of Georgia
Xiaoli Etienne: University of Idaho
Megan Sweitzer: U.S. Department of Agriculture - Economic Research Service
Richard Volpe: California Polytechnic State University
Wendy Zeng: U.S. Department of Agriculture - Economic Research Service

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract The advent of COVID-19 ended an era of stable US retail food prices that followed the world food price crisis of 2010–2012. Pandemic-related disruptions, avian influenza outbreaks, and the Russia-Ukraine war drove 2022 food-at-home inflation to its highest rate since 1974 (11.4%). In 2023, U.S. Department of Agriculture (USDA) economists responded to these changes by updating food price forecasts using statistical learning protocols to select time series models and prediction intervals to convey their uncertainty. We characterise the public good provided by these “adaptive” inflation forecasts and enhance them by incorporating exogenous variables to improve their precision and explanatory power. COVID-19’s arrival highlighted the value of adapting to the growing relevance of the all-items-less-food-and-energy ("core”) index, the money supply, and wages in predicting food prices. The strong relationships between food prices and core prices and the money supply indicate the sensitivity of food markets to macroeconomic forces and government policy decisions.

Date: 2025
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DOI: 10.1038/s41467-025-61660-x

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