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An Alternative Approach to Decisions under Uncertainty Using the Empirical Moment‐Generating Function
Author(s) -
Collender Robert Neil,
Chalfant James A.
Publication year - 1986
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.2307/1241557
Subject(s) - moment (physics) , nonparametric statistics , function (biology) , variance (accounting) , econometrics , exponential family , empirical distribution function , maximization , exponential function , q function , decision rule , expected utility hypothesis , cumulative distribution function , order (exchange) , mathematics , mathematical economics , mathematical optimization , economics , probability density function , statistics , mathematical analysis , physics , accounting , classical mechanics , evolutionary biology , biology , finance
Objections to mean‐variance analysis center on the requirement that the distributions be normal or the resulting inattention to higher‐order moments when the solution is viewed as an approximation. A nonparametric approach to decision making under uncertainty is developed in this paper using expected utility maximization, the exponential utility function, and an empirical moment‐generating function. A decision rule is obtained for land allocation under uncertainty which is not tied to a particular family of distributions and accounts for all moments of the distribution.