Stochastic efficiency analysis with risk aversion bounds: a simplified approach

Authors

  • J. Brian Hardaker,

  • James W. Richardson,

  • Gudbrand Lien,

  • Keith D. Schumann

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      Brian Hardaker is Emeritus Professor in the Graduate School of Agricultural and Resource Economics, University of New England, Armidale, New South Wales, Australia. James Richardson is Regents Professor and TAES Faculty Fellow in the Department of Agricultural Economics at Texas A&M University, College Station, Texas, USA. Gudbrand Lien is Senior Researcher at the Norwegian Agricultural Economics Research Institute, Oslo and at the Eastern Norway Research Institute, Lillehammer, Norway. Keith Schumann is Extension Associate in the Department of Agricultural Economics at Texas A&M University, College Station, Texas, USA. The authors are grateful to Jock Anderson and two anonymous referees for helpful comments on earlier drafts.


Abstract

A method of stochastic dominance analysis with respect to a function (SDRF) is described and illustrated. The method, called stochastic efficiency with respect to a function (SERF), orders a set of risky alternatives in terms of certainty equivalents for a specified range of attitudes to risk. It can be applied for conforming utility functions with risk attitudes defined by corresponding ranges of absolute, relative or partial risk aversion coefficients. Unlike conventional SDRF, SERF involves comparing each alternative with all the other alternatives simultaneously, not pairwise, and hence can produce a smaller efficient set than that found by simple pairwise SDRF over the same range of risk attitudes. Moreover, the method can be implemented in a simple spreadsheet with no special software needed.

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