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Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets

Published online by Cambridge University Press:  26 January 2015

Andrew M. McKenzie
Affiliation:
Department of Agricultural Economics and Agribusiness, University of Arkansas, Fayetteville, AR
Harold L. Goodwin Jr.
Affiliation:
Department of Agricultural Economics and Agribusiness, University of Arkansas, Fayetteville, AR
Rita I. Carreira
Affiliation:
Department of Agricultural Economics and Agribusiness, University of Arkansas, Fayetteville, AR
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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.

Type
Research Article
Copyright
Copyright © Southern Agricultural Economics Association 2009

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