Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T14:16:18.675Z Has data issue: false hasContentIssue false
  • English
  • Français

A Generalized Stochastic Dominance Program for the IBM PC

Published online by Cambridge University Press:  05 September 2016

Siew Goh ,
Chao-Chyuan Shih ,
Mark J. Cochran  and
Rob Raskin
Siew Goh
Affiliation:
Department of Agricultural Economics, University of Arkansas
Chao-Chyuan Shih
Affiliation:
Department of Agricultural Economics, University of Arkansas
Mark J. Cochran
Affiliation:
Department of Agricultural Economics, University of Arkansas
Rob Raskin
Affiliation:
Department of Atmospheric and Oceanic Science, University of Michigan
Get access Rights & Permissions [Opens in a new window]

Abstract

A microcomputer program to perform Generalized Stochastic Dominance (GSD), Quasi-Second Degree Dominance (SSD), and Quasi-First Degree Stochastic Dominance (FSD) is described. The program is designed to run on IBM-compatible personal computers with a Hercules or CGA graphics adapter. It is menu-driven and has options for GSD, quasi-FSD, quasi-SSD, graphics, and calculations of premiums associated with use of dominant distributions.

Keywords

IBM-compatible personal computersstochastic dominanceapplied-risk analysisexpected utility
Type
Submitted Articles
Information
Journal of Agricultural and Applied Economics , Volume 21 , Issue 2 , December 1989 , pp. 175 - 182
Copyright
Copyright © Southern Agricultural Economics Association 1989

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

Anaman, K. A., and Boggess, W.G.. “A Stochastic Dominance Analysis of Alternative Marketing Strategies for Mixed Crop Farms in North Florida.” So. J. Agr. Econ., 18(1986):257–266.Google Scholar
Bosch, D. and Eidman, V.. “Valuing Information When Risk Attitudes Are Nonneutral: An Application to Irrigation Scheduling.” Amer. J. Agr. Econ., 69(1987):658–668.CrossRefGoogle Scholar
Byerlee, D. and Anderson, J.R.. “Risk, Utility and the Value of Information in Farmer Decision Making.” Rev. Mkt. Agr. Econ., 50(1982):231–245.Google Scholar
Cochran, M.J., and Mjelde, J.W.. “Estimating the Value of Information with Stochastic Dominance: An Application from Agricultural Crop Management.” Texas Agricultural Experiment Station Technical Article TA 23245.1987.Google Scholar
Cochran, M.J.. “Stochastic Dominance: The State of the Art in Agricultural Economics” in Risk Analysis for Agricultural Production Firms: Concepts, Information Requirements and Policy Issues. Proceedings of the 1986 meetings of Regional Project S-180. Department of Agricultural Economics, Washington State University. August 1986.Google Scholar
Cochran, M.J., Robison, L.J., and Lodwick, W.. “Improving the Efficiency of Stochastic Dominance Techniques Using Convex Set Stochastic Dominance.” Amer. J. Agr. Econ., 67(1985):289–295.CrossRefGoogle Scholar
Danok, A.B., McCarl, B.A., and White, T.K.. “Machinery Selection Modeling Incorporation of Weather Variability.” Amer. J. Agr. Econ., 62(1980):700–708.CrossRefGoogle Scholar
Greene, C., Kramer, R., Norton, G., Rajotte, E., and McPherson, R.. “An Economic Analysis of Soybean Integrated Pest Management.” Amer. J. Agr. Econ., 67(1985):567–572.CrossRefGoogle Scholar
Hilton, R.W.. “The Determinants of Information Value: Synthesizing Some General Results.” Management Science, 27(1981):57–64.CrossRefGoogle Scholar
Holt, M.T., and Brandt, J.A.. “Combining Price Forecasting with Hedging of Hogs: An Evaluation Using Alternative Measures of Risk.” J. Futures Mkts., 5(1985):297–309.CrossRefGoogle Scholar
King, R.P., and Lybecker, D.W.. “Flexible, Risk Oriented Marketing Strategies for Pinto Bean Producers.” West. J. Agr. Econ., 8(1983):124–133.Google Scholar
King, R.P., and Oamek, G.E.. “Risk Management by Colorado Dryland Wheat Farmers and the Elimination of the Disaster Assistance Program.” Amer. J. Agr. Econ., 65(1983):247–255.CrossRefGoogle Scholar
King, R.P., and Robison, L.J.. “Implementation of the Interval Approach to the Measurement of Decision Maker Preferences.” Research Report 418, Agricultural Experiment Station, Michigan State University. November 1981.Google Scholar
Kramer, R.A.., and Pope, R.D.. “Participation in Farm Commodity Programs: A Stochastic Dominance Analysis.” Amer. J. Agr. Econ., 63(1981):119–128.CrossRefGoogle Scholar
Lemieux, C., Richardson, J., and Nixon, C.. “Federal Crop Insurance vs. ASCS Disaster Assistance for Texas High Plains Cotton Producers: An Application of Whole Farm Simulation.” West. J. Agr. Econ., 7(1982):141–153.Google Scholar
Meyer, J. “Choice Among Distributions.” J. Econ. Theory, 14(1977):326–336.CrossRefGoogle Scholar
Rister, M. E., Skees, J., and Black, J.R.. “Evaluating Use of Outlook Information in Grain Sorghum Storage Decisions.” So. J. Agr. Econ., 16(1984):151–158.Google Scholar
Tauer, L.W. “Use of Life Insurance to Fund the Farm Purchase from Heirs.” Amer. J. Agr. Econ., 67(1985):60–69.CrossRefGoogle Scholar
Zacharias, T.P., and Grube, A.H.. “An Economic Evaluation of Weed Control Methods Used in Combination With Crop Rotation: A Stochastic Dominance Approach.” N. Cent. J. Agr. Econ., 6(1984):113–120.Google Scholar
Zering, K.D., McCorkle, C.O., and Moore, C.V.. “The Utility of Multiple Peril Crop Agriculture.” W. J. Agr. Econ., 12(1987):50–59.Google Scholar