Accurately forecasting commodity prices and transportation costs can be a major benefit to market participants. This is especially true with U.S. agricultural export commodities, such as corn and soybeans, that are characterized by small margins but large volume. Traditionally, market participants have relied on naive methods for predicting barge rates, which represent the cost to transport the agriculture commodity along the Mississippi River system. A testable hypothesis is whether a spatial autoregressive model attempting to predict these barge rates will outperform a naive model. Results support the hypothesis that improved one-, two-, and five-week-ahead forecasts can be generated using this spatial model. The benefits of increased accuracy in barge rate forecasts are quantified in a simple trading scenario. Market participants can save 17%-29% on barge rate transportation costs depending on the river segment. Increased knowledge and the ability to more accurately predict barge rates is a benefit to all market participants and has a large affect on the prices of the agricultural commodities both domestically and abroad.
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