Food Price Inflation in the United States as a Complex Dynamic Economic System

Abstract

Abstract The issue of volatile food prices is a consistent problem for American consumers, as rising prices make it challenging to afford nutritious food that meets dietary standards. Various complex factors influence this price volatility, including economic conditions, weather patterns, global trade, energy prices, and more. Notably, the impact of food price increases is not equal for everyone. Low-income individuals and those in rural areas are disproportionately affected. A comprehensive understanding of the driving factors is essential to tackle this issue effectively. We employ advanced time-series techniques such as Vector Error Correction Models (VECM) and modern causal inference methods such as probabilistic graphical models implemented via machine learning and artificial intelligence approaches on monthly data from 2000 to 2021 to investigate the U.S. food price inflation issue. These methods help unravel the intricate dynamics among key variables driving food price inflation. The study aims to achieve several objectives. It intends to (1) clarify how factors influencing food price inflation in the U.S. change over time using VECM models, (2) establish causal relationships among interconnected variables to develop probabilistic graphical models using innovative search algorithms, and (3) create and validate forecasts related to U.S. food price inflation. The end goal is to provide actionable insights for policy design. Results show that food price inflation is heavily tied to commodity pricing and pricing for medical services. Additionally, historical decompositions for COVID-19 show ties between food price inflation and energy inflation.