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Abstract
Although Vector Autoregressive models are commonly used to forecast prices, specification
of these models remains an issue. Questions that arise include choice of variables and lag
length. This article examines the use of Forecast Error Variance Decompositions to guide the
econometrician’s model specification. Forecasting performance of Variance Autoregressive
models, generated from Forecast Error Variance Decompositions, is analyzed within
wholesale chicken markets. Results show that the Forecast Error Variance Decomposition
approach has the potential to provide superior model selections to traditional Granger
Causality tests.