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Predicting Pork Supplies: An Application of Multiple Forecast Encompassing

Published online by Cambridge University Press:  28 April 2015

Dwight R. Sanders  and
Mark R. Manfredo
Dwight R. Sanders
Affiliation:
Department of Agribusiness Economics, Southern Illinois University, Carbondale, IL
Mark R. Manfredo
Affiliation:
Morrison School of Agribusiness and Resource Management, Arizona State University, Mesa, AZ
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Abstract

Conditional efficiency or forecast encompassing is tested among alternative pork production forecasts using the method proposed by Harvey and Newbold. One-, two-, and three-quarter ahead pork production forecasts made by the United States Department of Agriculture (USDA), the University of Illinois and Purdue University Cooperative Extension Service, and those produced by a univariate time series model are evaluated. The encompassing tests provide considerably more information about forecast performance than a simple pair-wise test for equality of mean squared errors. The results suggest that at a one-quarter horizon, the Extension service forecasts encompass the competitors, but at longer horizons, a composite forecast may provide greater accuracy.

Keywords

composite forecastsforecast encompassingpork productionC53Q13

Information

Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 36 , Issue 3 , December 2004 , pp. 605 - 615
Copyright
Copyright © Southern Agricultural Economics Association 2004

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References

Armstrong, J.S.Principles of Forecasting: A Handbook for Researchers and Practitioners. Boston: Kluwer Academic Publishers, 2001.CrossRefGoogle Scholar
Ashley, R. “Statistically Significant Forecasting Improvements: How Much Out-of-Sample Data Is Likely Necessary?” International Journal of Forecasting 19(2003):229–39.CrossRefGoogle Scholar
Bailey, D.V., and Brorsen, B.W.. “Trends in the Accuracy of USDA Production Forecasts for Beef and Pork.” Journal of Agricultural and Resource Economics 23(1998):515–25.Google Scholar
Bates, J., and Granger, G.W.J.. “The Combination of Forecasts.” Operational Research Quarterly 20(1969):451–68.CrossRefGoogle Scholar
Bessler, D.A., and Chamberlain, P.J.. “Composite Forecasting with Dirichlet Priors.” Decision Sciences 19(1988):771–81.CrossRefGoogle Scholar
Brandt, J.A., and Bessler, D.A.. “Composite Forecasting: An Application with U.S. Hog Prices.” American Journal of Agricultural Economics 63(1981):135–40.CrossRefGoogle Scholar
Bunn, D.W.The Synthesis of Forecasting Models in Decision Analysis. Basel, Switzerland: Birkhausen 1978.Google Scholar
Clements, M.P. “Some Possible Directions for Future Research.” International Journal of Forecasting 19(2003):1–3.CrossRefGoogle Scholar
Diebold, F.X., and Mariano, R.S.. “Comparing Predictive Accuracy.” Journal of Business and Economic Statistics 13(1995):253–63.Google Scholar
Egelkraut, T.M., Garcia, P., Irwin, S.H., and Good, D.L.. “An Evaluation of Crop Forecast Accuracy for Corn and Soybeans: USDA and Private Information Agencies.” Journal of Agricultural and Applied Economics 35(2003):79–95.CrossRefGoogle Scholar
Granger, C.W.J. “Can We Improve the Perceived Quality of Economic Forecasts?” Journal of Applied Econometrics 11(1996):455–73.3.0.CO;2-E>CrossRefGoogle Scholar
Granger, C.W.J. and Newbold, P.. Forecasting Economic Time Series, 2nd ed. New York: Academic Press, 1986.Google Scholar
Hafer, R.W., and Hein, S.E.. “Comparing Futures and Survey Forecasts of Near-Term Treasury Bill Rates.” Federal Reserve Bank of St. Louis Review 71 (May/June 1989):33–42.Google Scholar
Harvey, D., Leybourne, S., and Newbold, P.. “Testing the Equality of Prediction Mean Squared Errors.” International Journal of Forecasting 13(1997):281–91.CrossRefGoogle Scholar
Harvey, D., “Tests for Forecast Encompassing.” Journal of Business and Economic Statistics 16(1998):254–59.Google Scholar
Harvey, D.I., and Newbold, P.. “Tests for Multiple Forecast Encompassing.” Journal of Applied Econometrics 15(2000):471–82.3.0.CO;2-X>CrossRefGoogle Scholar
Hurt, C. “Hog Producers Gearing Up For Expansion.” Livestock Price Outlook. Urbana, IL: Purdue University and University of Illinois, October 2000.Google Scholar
Johnson, S.R., and Rausser, G.. “Composite Forecasting in Commodity Systems.” New Directions in Econometric Modeling and Forecasting in U.S. Agriculture. Rausser, G., ed. New York: North Holland Publishing, 1982.Google Scholar
Kastens, T.L., Schroeder, T.C., and Plain, R.. “Evaluation of Extension and USDA Price and Production Forecasts.” Journal of Agricultural and Resource Economics 23(1998):244–61.Google Scholar
Leuthold, R.M. “On the Use of Theil's Inequality Coefficients.” American Journal of Agricultural Economics 57(1975):344–46.CrossRefGoogle Scholar
Meehl, P.E.Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. Minneapolis: University of Minnesota Press, 1954.CrossRefGoogle Scholar
Mills, T.C., and Pepper, G.T. “Assessing the Forecasters: An Analysis of the Forecasting Records of the Treasury, the London Business School and the National Institute.” International Journal of Forecasting 15(1999):247–57.CrossRefGoogle Scholar
Sanders, D.R., and Manfredo, M.R.. “USDA Production Forecasts for Pork, Beef, and Broilers: An Evaluation.” Journal of Agricultural and Resource Economics 27(2002):114–27.Google Scholar
U.S. Department of Agriculture. World Agricultural Supply and Demand Estimates. Internet site: http://www.usda.gov/oce/waob/wasde/wasde.htm (Accessed July 15, 2003).Google Scholar
Wang, Z., and Bessler, D.A.. “The Homogeneity Restriction and Forecasting Performance of VAR-type Demand Systems.” Journal of Forecasting 21(2002):193–206.CrossRefGoogle Scholar