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Abstract

Advanced methods that combine directed acyclic graphs with Bernanke structural vector autoregression models are applied to a monthly system of three U.S. soy-based markets: for soybeans upstream and for the two soybean co-products soy meal and soy oil further downstream. Analyses of the impulse-response function and forecast error variance decompositions provide updated estimates of market-elasticity parameters that drive these markets and updated policy-relevant information on how these monthly markets run and dynamically interact. Results characterize impacts on the three U.S. soy-based markets of increases in U.S. prices of soy meal and soybeans.

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