Parametric And Non-Parametric Crop Yield Distributions And Their Effects On All-Risk Crop Insurance Premiums
AbstractNormal, gamma and beta distributions are applied to 609 crop yield histories of Ontario farmers to determine which, if any, best describe crop yields. In addition, a distribution free non-parametric kernel estimator was applied to the same data to determine its efficiency in premium estimation relative to the three parametric forms. Results showed that crop yields are most likely to be described by a beta distribution but only for 50% of those tested. In terms of efficiency in premium estimation, minimum error criteria supports use of a kernel estimator for premium setting. However, this gain in efficiency comes at the expense of added complexity.
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Bibliographic InfoPaper provided by University of Guelph, Department of Food, Agricultural and Resource Economics in its series Working Papers with number 34129.
Date of creation: 1999
Date of revision:
crop insurance; crop yield distributions; kernel; Crop Production/Industries; Risk and Uncertainty;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Antle, John M, 1983. "Testing the Stochastic Structure of Production: A Flexible Moment-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 192-201, July.
- Lanoue, Christopher & Sherrick, Bruce J. & Woodard, Joshua D. & Paulson, Nicholas D., 2010. "Evaluating Yield Models for Crop Insurance Rating," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado 61761, Agricultural and Applied Economics Association.
- Lee, Sangjun & Zhao, Jinhua & Thornsbury, Suzanne, 2013. "Extreme Events and Land Use Decisions under Climate Change in Tart Cherry Industry in Michigan," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150568, Agricultural and Applied Economics Association.
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