Dynamic Adjustment and Distributional Integration in U.S. Cash Corn Markets

This paper examines long-run price integration and short-run distributional dynamics across nine major U.S. corn cash markets. Although a large literature documents spatial price integration within commodity markets, prices observed at different geographic locations rarely move in perfectly identical ways, leaving scope for persistent gaps in both equilibrium adjustment and short-run behavior. Using monthly corn cash price data, we first estimate a Vector Error Correction Model (VECM) to characterize long-run linkages and adjustment dynamics among the markets. We then examine short-run dynamics using autoregressive models and Bayes factor model comparison based on Pólya tree methods, applied to both log price series and autoregressive residuals. The results provide evidence of eight cointegrating relationships, indicating strong long-run integration across the nine markets. At the same time, VECM model shows that lagged changes in Minneapolis prices are statistically significant in all market equations, suggesting that Minneapolis contains broad predictive information for subsequent price movements across the system. Our autoregressive model and distributional comparisons further reveal that several markets—including Memphis, Minneapolis, Kansas City, Chicago, and Central Illinois—share similar dynamics and residual distributions, while other locations exhibit distinct distributional patterns. Overall, the evidence points to strong long-run integration among U.S. corn cash markets, alongside persistent geographic heterogeneity in short-run dynamics and distributional behavior in certain areas.