Hostname: page-component-745bb68f8f-b6zl4 Total loading time: 0 Render date: 2025-01-11T13:15:04.419Z Has data issue: false hasContentIssue false
  • English
  • Français

Generalized Stochastic Dominance: An Empirical Examination

Published online by Cambridge University Press:  09 September 2016

Bruce A. McCarl
Bruce A. McCarl*
Affiliation:
Texas A&M University
Get access Rights & Permissions [Opens in a new window]

Abstract

Use of generalized stochastic dominance (GSD) requires one to place lower and upper bounds on the risk aversion coefficient. This study showed that breakeven risk aversion coefficients found assuming the exponential utility function delineate the places where GSD preferences switch between prospects. However, between these break points, multiple, overlapping GSD intervals can be found. Consequently, when one does not have risk aversion coefficient information, discovery of breakeven coefficients instead of GSD use is recommended. The investigation also showed GSD results are insensitive to wealth and data scaling but are sensitive to rounding.

Keywords

riskgeneralized stochastic dominancerisk aversion coefficients
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 22 , Issue 2 , December 1990 , pp. 49 - 55
Copyright
Copyright © Southern Agricultural Economics Association 1990

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

Danok, A.B. McCarl, B.A., and White, T.K.. “Machinery Selection Modeling: Incorporation of Weather Variability”. Am. J. Agr. Econ., 62(1980):700–708.Google Scholar
Goh, S. Shih, S., Cochran, M.J., and Raskin, R.. “A Generalized Stochastic Dominance Program for the IBM PC”. So. J. Agr. Econ., 21(1989):175–182.Google Scholar
Hammond, J.S. “Simplifying the Choice Between Uncertain Prospects Where Preference is Nonlinear”. Management Sci., 20(1974): 1047–1072.Google Scholar
Kamien, M.I. and Schwartz, N.L.. Dynamic Optimization: The Calculus of Variations and Optimal Control in Economics and Management. New York, NY: Elsevier Scient Publishing Co., 1981.Google Scholar
King, R.P. and> Robison, L.J.. “An Interval Approach to the Measurement of Decision Maker Preference”. Am. J. Agr. Econ., 63(1981):510–520.Google Scholar
Klemme, R.M. “A Stochastic Dominance Comparison of Reduced Tillage Systems in Corn and Soybean Productions Systems under Risk”. Am. J. Agr. Econ., 67(1985):550–557.CrossRefGoogle Scholar
Kramer, R.A. and Pope, R.D.. “Participation in Farm Commodity Programs: A Stochastic Dominance Analysis”. Am. J. Agr. Econ., 63(1981):119–128.Google Scholar
McCarl, B.A. “Meyeroot Program Documentation”. Unpublished Computer Documentation, Department of Agricultural Economics, Texas A&M University, College Station, TX., 1990.Google Scholar
McCarl, B.A. “Preference Among Risky Prospects Under Constant Risk Aversion”. So. J. Agr. Econ., 20(2)(1988):25–33.Google Scholar
McCarl, B.A. and Bessler, D.A.. “Estimating an Upper Bound on the Pratt Risk Aversion Coefficient When the Utility Function is Unknown”. Austral. J. Agr. Econ., 33(1989):56–63.Google Scholar
Meyer, J. “Choice Among Distributions”. J. Econ. Theory, 14(1977):326–336.Google Scholar
Meyer, J. STODOM Computer Program, Department of Economics, Texas A&M 1975.Google Scholar
Pratt, J.W. “Risk Aversion in the Small and in the Large”. Econometrica, 32(1964): 122–136.Google Scholar
Raskin, R. and Cochran, M.J.. “Interpretation and Transformations of Scale for the Pratt-Arrow Absolute Risk Aversion Coefficient: Implications for Stochastic Dominance”. West. J. Agr. Econ., 11(1986):204–210.Google Scholar
Tolley, H.D. and Pope, R.D.. “Testing for Stochastic Dominance”. Am. J. Agr. Econ., 70(1988):693–700.CrossRefGoogle Scholar
Von Neumann, J. and Morgenstern, O.. Theory of Games and Economic Behavior. Princeton: Princeton University Press, 1947. Google Scholar