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Incorporating Government Program Provisions into a Mean-Variance Framework

Published online by Cambridge University Press:  05 September 2016

Gregory M. Perry
Affiliation:
Department of Agricultural and Resource Economics, Oregon State University
M. Edward Rister
Affiliation:
Department of Agricultural Economics, Texas A & M University
James W. Richardson
Affiliation:
Department of Agricultural Economics, Texas A & M University
David A. Bessler
Affiliation:
Department of Agricultural Economics, Texas A & M University

Abstract

E-V studies traditionally have relied on historical data to calculate returns and variance. Historical data may not fully reflect current conditions, particularly when decisions involve government-supported crops. This paper presents a method for calculating mean and variance using subjectively-estimated data. The method is developed for both government-supported and non-program crops. Comparisons to alternative methods suggest the approach provides reasonable accuracy.

Type
Submitted Articles
Copyright
Copyright © Southern Agricultural Economics Association 1989

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References

Anderson, J.R., Dillon, J.L., and Hardaker, B.. Agricultural Decision Analysis. Ames, IO: The Iowa State University Press, 1977.Google Scholar
Arrow, K.J. “Comment on the Portfolio Approach to the Demand for Money and Other Assets.Rev. Econ. Stat, 45(1963):2427.Google Scholar
Bessler, D.A. “Subjective Probability.In Risk Management in Agriculture. Ed. Barry, P.J.. Ames, IO: The Iowa State University Press, 1984.Google Scholar
Boggess, W.G., Lynne, G.D., Jones, J.W., and Swaney, D.P.. “Risk-Return Assessment of Irrigation Decisions in Humid Regions:So. J. Agr. Econ., 15(1983):135144.Google Scholar
Bohrnstedt, G.W., and Goldberger, A.S.. “On the Exact Covariance of Products of Random Numbers.J. Amer. Stat. Assoc, 64(1969):14391442.Google Scholar
Buccola, S.T.Testing for Nonnormality in Farm Net Returns.Amer. J. Agr Econ., 68(1986):334343.CrossRefGoogle Scholar
Burt, O. R., and Finley, R.M.. “Statistical Analysis of Identities in Random Variables.Amer. J. Agr. Econ., 50(1968):734744.Google Scholar
Freund, R J. “The Introduction of Risk into a Programming Model.Econometrica, 24(1956):253264.Google Scholar
Glaser, L.K. Provisions of the Food Security Act of 1985. Washington D.C. U.S. Department of Agriculture, Agri. Informa. Rep. No. 498, April 1986.Google Scholar
Hadar, J. and Russell, W.R.. “Rules for Ordering Uncertain Prospects.Amer. Econ. Rev., 59(1969):2534.Google Scholar
Huber, G.P. “Methods for Quantifying Subjective Probabilities and Multiattribute Utilities.Decision Sciences, 5(1974):430458.Google Scholar
Knuth, D.E. The Art of Computer Programming. Vol.2. Reading, Mass.: Addison-Wesley, 1969.Google Scholar
Levy, H. and Markowitz, H.M.. “Approximating Expected Utility by a Function of Mean and Variance.Amer. Econ. Rev., 69(1979):308317.Google Scholar
Lin, W., Dean, G.W., and Moore, C.V.. “An Empirical Test of Utility vs. Profit Maximization in Agricultural Production.Amer. J. Agr. Econ., 56(1974):497508.Google Scholar
Markowitz, H.M. Portfolio Selection: Efficient Distribution of Investments. New York: John Wiley & Sons, 1959.Google Scholar
Meyer, J.Two Moment Decision Models and Expected Utility Maximization.Amer. Econ. Rev., 77(1987):421430.Google Scholar
Musser, W.N., and Stamoulis, K.G.. “Evaluating the Food and Agriculture Act of 1977 with Firm Quadratic Risk Programming.Amer. J. Agr. Econ., 63(1981):448456.Google Scholar
Persaud, T. and Mapp, H.P. Jr. “Analysis of Alternative Production and Marketing Strategies in Southwestern Oklahoma: A MOTAD Approach.” Paper presented at the 1980 Annual Meeting of Regional Research Proj. W-149, Tucson, AZ. January 16-18, 1980.Google Scholar
Poirier, D.J. “Frequentists and Subjectivist Perspectives on the Problems of Model Building in Economics.J. Econ. Per., 2(1988):121144.Google Scholar
Porter, R.B., and Gaumnitz, J.E.. “Stochastic Dominance vs. Mean-Variance Portfolio Analysis: An Empirical Evaluation.Amer. Econ. Rev., 62(1972):438446.Google Scholar
Pratt, J.W.. “Risk Aversion in the Small and in the Large.Econometrica, 32(1964):122136.CrossRefGoogle Scholar
Richardson, J.W., and Nixon, C.J.. Description of FLIPSIM V: A General Firm Level Policy Simulation Model. Texas Agri. Exp. Sta. Bull. B-1528. College Station, July 1986.Google Scholar
Scott, J.T. Jr, and Baker, C.B.. “A Practical Way to Select an Optimum Farm Plan Under Risk.Amer. J. Agri. Econ., 54(1972):657660.Google Scholar
Stovall, J.G. “Income Variation and the Selection of Enterprises.J. Farm Econ., 48(1966):15751579.Google Scholar
Tauer, L.W. “Target MOTAD.Amer. J. Agri. Econ., 65(1983):606610.Google Scholar
Tew, B.V., and Boggess, W.G.. “Risk Return Assessment of Irrigation Decisions in Humid Regions: An Extension.So. J. Agri. Econ., 16(1984):159160.Google Scholar
Thompson, R.L. “Global Trends in Supply and Demand.” Agriculture Outlook, AO-127 (January 1987):26 Google Scholar
Tversky, A. and Kahneman, D.. “Availability: A Heuristic for Judging Frequency and Probability.Cognitive Psychology, 5(1973):201232.CrossRefGoogle Scholar