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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 ,
Harold L. Goodwin Jr.  and
Rita I. Carreira
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.

Keywords

broiler marketsDAGsforecastingmarket structureVARC53D4L1Q00
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 41 , Issue 1 , April 2009 , pp. 227 - 240
Copyright
Copyright © Southern Agricultural Economics Association 2009

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References

Awokuse, T.O., and Bessler, D.A. “VAR, Policy Analysis, and Directed Graphs: An Application to the US Economy.” Journal of Applied Econometrics 5(2003):1–24.Google Scholar
Bernanke, B. “Alternative Explanations of the Money-Income Correlation.” Carnegie-Rochester Conference Series on Public Policy 25(1986):49–100.Google Scholar
Bessler, D.A. “An Analysis of Dynamic Economic Relationships: An Application to the U.S. Hog Market.” Canadian Journal of Agricultural Economics 32(1984):109–24.Google Scholar
Bessler, D.A., and Akleman, D.G. “Farm Prices, Retail Prices, and Directed Graphs: Results for Pork and Beef.” American Journal of Agricultural Economics 80(1998):1144–49.Google Scholar
Carnot, N., Koen, V., and Tissot, B.. Economic Forecasting. New York: Palgrave MacMillan, Ltd., 2005.Google Scholar
Diebold, EX., and Mariano, R.S. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 13(1995):253–63.Google Scholar
Enders, W. Applied Econometric Time Series, 2nd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2004.Google Scholar
Estima. RATS Version 6 User's Guide and Reference Manual. Evanston, IL: Estima, 2004.Google Scholar
Goodwin, H.L., McKenzie, A.M., and Djunaidi, H. “Which Broiler Part is the Best Part?” Journal of Agricultural and Applied Economics 35(2003):483–95.Google Scholar
Hsiao, C., “Causality Tests in Econometrics.” Journal of Economic Dynamics & Control 1(1979):321–46.Google Scholar
Hsiao, C., “Autoregressive Modeling and Causal Ordering of Economic Variables.” Journal of Economic Dynamics & Control 4(1982):243–59.Google Scholar
Kling, J.L., and Bessler, D.A. “A Comparison of Multivariate Forecasting Procedures for Economic Time Series.” International Journal of Forecasting 1(1985):5–24.Google Scholar
McKenzie, A.M. “The Effects of Barge Shocks on Soybean Basis Levels in Arkansas: A Study of Market Integration.” agribusiness International Journal (Toronto, Ont.) 21(2005):37–52.Google Scholar
National Chicken Council. NCC Statistics and Research. Internet Site: http://www.national chickencouncil.com/statistics/. (Accessed October 2007).Google Scholar
Sims, C.A. “Are Forecasting Models Usable for Policy Analysis?” Federal Reserve Bank of Minneapolis Quarterly Review 10(1986):2–16.Google Scholar
Spirtes, P., Glymour, C., and Scheines, R.. Causation, Prediction, and Search, 2nd ed. New York: The MIT Press, 2001.Google Scholar
Spirtes, P., Scheines, R., Meek, C., Richardson, T., Glymour, C., Boomsam, A., and Hoijtink, H.. TETRAD II Version 3.1 Tools for Causal Modeling. Pittsburgh, PA: Carnegie Mellon University, 1999.Google Scholar
Urner Barry Publications, Inc. Umer Barry's Price-Current. Toms River, N.J. Various Issues, January 1989-June 2007.Google Scholar
U.S. Department of Agriculture/Economic Research Service. Background Statistics on U.S. Broiler Industry. Internet site: http://www.ers. usda.gov/news/broilercoverage.htm (Accessed July 2007).Google Scholar