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Bayesian Composite Qualitative Forecasting: Hog Prices Again

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  • Jeffrey H. Dorfman

Abstract

A new method for forming composite qualitative forecasts is presented. A set of qualitative forecasts is evaluated using auxiliary logit models to predict the probability of each forecast's correctness. Individual model forecasts are then combined on the basis of normalized values of these probabilities. This method is demonstrated with three sets of forecasts on the direction of change in hog prices (up or down). The application shows that without any information on the manner in which the individual forecasts are generated this method can form a composite forecast that is superior according to a variety of metrics for evaluating qualitative forecasts. Copyright 1998, Oxford University Press.

Suggested Citation

  • Jeffrey H. Dorfman, 1998. "Bayesian Composite Qualitative Forecasting: Hog Prices Again," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(3), pages 543-551.
  • Handle: RePEc:oup:ajagec:v:80:y:1998:i:3:p:543-551
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    File URL: http://hdl.handle.net/10.2307/1244556
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    Cited by:

    1. Lota D. Tamini & Jean‐Philippe Gervais, 2005. "Developing Economic Indexes for the Quebec Hog/Pork Industry," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 53(1), pages 1-23, March.
    2. Robledo, Carlos W. & Zapata, Hector O. & McCracken, Michael, 2001. "New Mse Tests For Evaluating Forecasting Performance: Empirics And Bootstrap," 2001 Annual meeting, August 5-8, Chicago, IL 20686, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Ker, Alan. P & Tolhurst, Tor & Liu, Yong, 2015. "Rating Area-yield Crop Insurance Contracts Using Bayesian Model Averaging and Mixture Models," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205211, Agricultural and Applied Economics Association.
    4. Holloway, Garth & Nicholson, Charles & Delgado, Chris & Staal, Steve & Ehui, Simeon, 2004. "A revised Tobit procedure for mitigating bias in the presence of non-zero censoring with an application to milk-market participation in the Ethiopian highlands," Agricultural Economics, Blackwell, vol. 31(1), pages 97-106, July.
    5. Hutson, Mark & Joutz, Fred & Stekler, Herman, 2014. "Interpreting and evaluating CESIfo's World Economic Survey directional forecasts," Economic Modelling, Elsevier, vol. 38(C), pages 6-11.
    6. Norwood, F. Bailey & Lusk, Jayson L. & Brorsen, B. Wade, 2004. "Model Selection for Discrete Dependent Variables: Better Statistics for Better Steaks," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(3), pages 1-16, December.
    7. Li, Anzhi & Dorfman, Jeffrey H., 2014. "Composite Qualitative Forecasting of Futures Prices: Using One Commodity to Help Forecast Another," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169790, Agricultural and Applied Economics Association.
    8. Lusk, Jayson L. & Norwood, F. Bailey & Brorsen, B. Wade, 2004. "Forecasting Limited Dependent Variables: Better Statistics For Better Steaks," 2004 Annual Meeting, February 14-18, 2004, Tulsa, Oklahoma 34612, Southern Agricultural Economics Association.

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