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A Flexible Parametric Family for the Modeling and Simulation of Yield Distributions

Published online by Cambridge University Press:  26 January 2015

Octavio A. Ramirez ,
Tanya U. McDonald  and
Carlos E. Carpio
Octavio A. Ramirez
Affiliation:
Department of Agricultural and Applied Economics, University of Georgia, Athens, GA
Tanya U. McDonald
Affiliation:
Department of Agricultural Economics and Agricultural Business, New Mexico State University, Las Cruces, NM
Carlos E. Carpio
Affiliation:
Department of Applied Economics and Statistics, Clemson University, Clemson, SC
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Abstract

The distributions currently used to model and simulate crop yields are unable to accommodate a substantial subset of the theoretically feasible mean-variance-skewness-kurtosis (MVSK) hyperspace. Because these first four central moments are key determinants of shape, the available distributions might not be capable of adequately modeling all yield distributions that could be encountered in practice. This study introduces a system of distributions that can span the entire MVSK space and assesses its potential to serve as a more comprehensive parametric crop yield model, improving the breadth of distributional choices available to researchers and the likelihood of formulating proper parametric models.

Keywords

risk analysisparametric methodsyield distributionsyield modeling and simulationyield nonnormalityC15C16C46C63
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 42 , Issue 2 , May 2010 , pp. 303 - 319
Copyright
Copyright © Southern Agricultural Economics Association 2010

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