Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T05:20:13.112Z Has data issue: false hasContentIssue false
  • English
  • Français

Forecasting Agricultural Prices Using a Bayesian Composite Approach

Published online by Cambridge University Press:  28 April 2015

Christopher S. McIntosh  and
David A. Bessler
Christopher S. McIntosh
Affiliation:
Agricultural Economics at the University of Georgia
David A. Bessler
Affiliation:
Agricultural Economics at Texas A&M University
Get access Rights & Permissions [Opens in a new window]

Abstract

Forecast users and market analysts need quality forecast information to improve their decision-making abilities. When more than one forecast is available, the analyst can improve forecast accuracy by using a composite forecast. One of several approaches to forming composite forecasts is a Bayesian approach using matrix beta priors. This paper explains the matrix beta approach and applies it to three individual forecasts of U.S. hog prices. The Bayesian composite forecast is evaluated relative to composites made from simple averages, restricted least squares, and an adaptive weighting technique.

Keywords

composite forecastingBayesianmatrix betaoutperformance
Type
Submitted Articles
Information
Journal of Agricultural and Applied Economics , Volume 20 , Issue 2 , December 1988 , pp. 73 - 80
Copyright
Copyright © Southern Agricultural Economics Association 1988

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ashley, R., Granger, C. W. J., and Schmalensee, R.. “Advertising and Aggregate Consumption: An Analysis of Causality.” Econometrica, 48:5 (1980):1149–1167.CrossRefGoogle Scholar
Bates, J.M., and Granger, C. W. J.. “The Combination of Forecasts.” Oper. Res. Quart., 20(1969):451–468.CrossRefGoogle Scholar
Bessler, D.A., and Brandt, J. A.. “Forecasting Livestock Prices with Individual and Composite Methods.” Applied Economics, 13(1981):513–522.CrossRefGoogle Scholar
Bessler, D.A., and Chamberlain, P. J.. “On Bayesian Composite Forecasting.” Omega, 15(1987):43–48.CrossRefGoogle Scholar
Bunn, D.W.. The Synthesis of Forecasting Models in Decision Analysis. Stuttgart: Burkhauser Verlag Basel, 1978.Google Scholar
Chicago Mercantile Exchange. Yearbook. Chicago, IL, 1973-1987.Google Scholar
Clemen, R.T., and Winkler, R. L.. “Combining Economic Forecasts.” J. Bus. and Econ. Stat., 4(1986):39–46.Google Scholar
DeGroot, M.H.. Optimal Statistical Decisions. New York: McGraw-Hill Book Company, 1970.Google Scholar
Doan, T.A., and Litterman, R. B.. Regression Analysis of Time Series. Minneapolis, MN: VAR Econometrics, 1984.Google Scholar
Grimes, G.Missouri Agricultural Outlook Letter. University of Missouri, Columbia, MO. Issued monthly.Google Scholar
U.S. Department of Agriculture. Livestock and Meat Statistics. Washington D.C., 1958-1987.Google Scholar