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On Bayesian composite forecasting

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  • Bessler, David A
  • Chamberlain, Peter J

Abstract

Often decision makers have several forecasts of an uncertain and operationally relevant random variable. A rich literature now exists which argues that in this situation the decision maker should consider forming a forecast as a weighted average of each of the individual forecasts. In this paper, composite forecasting is discussed in a Bayesian context. The ability of the user to control the impact of the data on his composite weights is illustrated by an example using expert opinion forecasts of US hog prices.

Suggested Citation

  • Bessler, David A & Chamberlain, Peter J, 1987. "On Bayesian composite forecasting," Omega, Elsevier, vol. 15(1), pages 43-48.
    • Handle: RePEc:eee:jomega:v:15:y:1987:i:1:p:43-48
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    Cited by:

    1. Luis Manuel León Anaya & Víctor Manuel Landassuri Moreno & Héctor Rafael Orozco Aguirre & Maricela Quintana López, 2018. "Predicción del IPC mexicano combinando modelos econométricos e inteligencia artificial," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 13(4), pages 603-629, Octubre-D.
    2. McIntosh, Christopher S. & Bessler, David A., 1988. "Forecasting Agricultural Prices Using a Bayesian Composite Approach," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 20(2), pages 73-80, December.
    3. Mostaghimi, Mehdi, 1996. "Combining ranked mean value forecasts," European Journal of Operational Research, Elsevier, vol. 94(3), pages 505-516, November.

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