The article examines wheat prices in different U.S. markets to see how they are connected. By comparing different forecasting methods, the researchers found that using a specific type of model can lead to more accurate long-term predictions. Specifically, using a levels vector autoregression model gives lower error variance over time, even though it may have a higher error bias initially. This suggests that understanding the relationships between wheat prices in different markets can help make better predictions about future prices.