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Characterizing Uncertain Outcomes with the Restricted HT Transformation

Published online by Cambridge University Press:  28 April 2015

L. Joe Moffitt
L. Joe Moffitt*
Affiliation:
Department of Resource Economics, University of Massachusetts, Amherst MA
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Abstract

Restrictions on the hyperbolic trigonometric (HT) transformation are imposed to guarantee that a probability density function is obtained from maximum likelihood estimation. Performance of the restricted HT transformation using data generated from normal, beta, gamma, logistic, log-normal, Pareto, Weibull, order statistic, and bimodal populations is investigated via sampling experiments. Results suggest that the restricted HT transformation is sufficiently flexible to compete with the actual population distributions in most cases. Application of the restricted HT transformation is illustrated by characterizing uncertain net income per acre for community-supported agriculture farms in the northeastern United States.

Keywords

farm managementhyperbolic trigonometric transformationuncertaintyC2Q1
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 34 , Issue 3 , December 2002 , pp. 417 - 429
Copyright
Copyright © Southern Agricultural Economics Association 2002

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References

Akaike, H. “Information Theory and an Extension of the Maximum Likelihood Principle.” Proceedings of the 2nd International Symposium on Information Theory. Petron, N. and Csadki, F., eds., pp. 267-81. Budapest: Akad emiai Kiado, 1973.Google Scholar
D'Agostino, R.B., Belanger, A., and D'Agostino, R.B. Jr. “A Suggestion for Using Powerful and Informative Tests of Normality.” American Statistician 44(1990):316-21.Google Scholar
Johnson, N.L., and Kotz, S.. Distributions in Statistics. New York: John Wiley & Sons, 1970.Google Scholar
Kelvin, R.Community Supported Agriculture on the Urban Fringe: Case Study and Survey. Kutztown, PA: Rodale Institute Research Center, 1994.Google Scholar
McDonald, J.D., Moffitt, L.J., and Willis, C.E.. “On Application of Mean-Gini Stochastic Efficiency Analysis.” Australian Journal of Agricultural and Resource Economics 41(1997):45–62.CrossRefGoogle Scholar
Pollak, R.A., and Wales, T.J.. “The Likelihood Dominance Criterion.” Journal of Econometrics 47(1991):227-42.CrossRefGoogle Scholar
Sanneh, N., Moffitt, L.J., and Lass, D.A.. “Stochastic Efficiency Analysis of Community Supported Agriculture Core Management Options.” Journal of Agricultural and Resource Economics 26(2001):417-30.Google Scholar
Taylor, C.R. “A Flexible Method for Empirically Estimating Probability Functions.” Western Journal of Agricultural Economics 9(1984):66–76.Google Scholar
Vuong, Q.H. “Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses.” Econometrica 57(1989):307-33.CrossRefGoogle Scholar
Yassour, J., Zilberman, D., and Rausser, G.C.. “Optimal Choices Among Alternative Technologies with Stochastic Yield.” American Journal of Agricultural Economics 63(1981):718-23.CrossRefGoogle Scholar