Cointegration Between U.S. Wheat Markets

Average monthly price data from twelve hinterland markets and the Houston port price for wheat are studied in a cointegration framework using the Engle‐Granger “two‐step” procedure and Johansen's maximum likelihood procedure. Out‐of‐sample forecasts from an error correction model are compared to those from a vector autoregression fit to levels and a univariate autoregression fit to first differences. This comparison suggests that modeling these (cointegrated) data as a levels vector autoregression, rather than as an error‐correction process, results in significantly higher error bias, but lower error variance, at long horizons.