Efficient Portfolios of Market Advisory Services: An Application of Shrinkage Estimators
A Bayesian hierarchical model was employed to estimate individual expected pricing performance for market advisory programs in corn and soybeans. Performance is defined as the difference between the price/revenue obtained by following the program's marketing recommendations and the average price/revenue offered by the market along the marketing window. The estimates obtained from this model are weighted averages of traditional separate and pooled estimates. Based on the sample employed, the most reasonable individual estimates for expected pricing performance imply a substantial shrinkage towards pooled values. The Bayesian estimates for expected pricing performance range from ¢9/bu to ¢9/bu for corn, from ¢11/bu to ¢17/bu for soybeans and $0.4/acre to $11/acre for revenue. Bayesian estimates are employed in the construction of efficient portfolios of advisory programs. Results suggest that farmers can benefit from following the marketing recommendations of advisory programs portfolios.
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- Andrew Gelman, 2004. "Prior distributions for variance parameters in hierarchical models," EERI Research Paper Series EERI_RP_2004_06, Economics and Econometrics Research Institute (EERI), Brussels.
- Andrew Gelman, 2004. "Prior distributions for variance parameters in hierarchical models," Econometrics 0404001, EconWPA.
- Isengildina, Olga & Pennings, Joost M.E. & Irwin, Scott H. & Good, Darrel L., 2004. "Crop Farmers’ Use of Market Advisory Services," AgMAS Project Research Reports 37489, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
- Silvina M. Cabrini & Brian G. Stark & Hayri Önal & Scott H. Irwin & Darrel L. Good & João Martines-Filho, 2004. "Efficiency Analysis of Agricultural Market Advisory Services: A Nonlinear Mixed-Integer Programming Approach," Manufacturing & Service Operations Management, INFORMS, vol. 6(3), pages 237-252, December.
- Jorion, Philippe, 1986. "Bayes-Stein Estimation for Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(03), pages 279-292, September.
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