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Parametric And Non-Parametric Crop Yield Distributions And Their Effects On All-Risk Crop Insurance Premiums

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  • Turvey, Calum G.
  • Zhao, Jinhua

Abstract

Normal, 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.

Suggested Citation

  • Turvey, Calum G. & Zhao, Jinhua, 1999. "Parametric And Non-Parametric Crop Yield Distributions And Their Effects On All-Risk Crop Insurance Premiums," Working Papers 34129, University of Guelph, Department of Food, Agricultural and Resource Economics.
  • Handle: RePEc:ags:uguewp:34129
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    File URL: http://purl.umn.edu/34129
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    References listed on IDEAS

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    1. Richard H. Day, 1965. "Probability Distributions of Field Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 47(3), pages 713-741.
    2. Gallagher, Paul W., 1986. "U. S. Corn Yield Capacity and Probability: Estimation and Forecasting with Non-Symmetric Disturbances," Staff General Research Papers Archive 10780, Iowa State University, Department of Economics.
    3. Carl H. Nelson & Paul V. Preckel, 1989. "The Conditional Beta Distribution as a Stochastic Production Function," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 370-378.
    4. Rulon D. Pope & Rod F. Ziemer, 1984. "Stochastic Efficiency, Normality, and Sampling Errors in Agricultural Risk Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(1), pages 31-40.
    5. 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.
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    Citations

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    Cited by:

    1. 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.
    2. Woodard, Joshua D. & Chiu Verteramo, Leslie & Miller, Alyssa P., 2015. "Adaptation of U.S. Agricultural Production to Drought and Climate Change," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205903, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    3. Jing Wang & Feng Fang & Qiang Zhang & Jinsong Wang & Yubi Yao & Wei Wang, 2016. "Risk evaluation of agricultural disaster impacts on food production in southern China by probability density method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1605-1634, September.
    4. repec:fgv:epgrbe:v:71:y:2017:i:4:a:27375 is not listed on IDEAS
    5. 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.
    6. Vitor Ozaki & Barry Goodwin & Ricardo Shirota, 2008. "Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts," Applied Economics, Taylor & Francis Journals, vol. 40(9), pages 1151-1164.

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