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

Listed author(s):
  • Turvey, Calum G.
  • Zhao, Jinhua

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.

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Paper provided by University of Guelph, Department of Food, Agricultural and Resource Economics in its series Working Papers with number 34129.

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Date of creation: 1999
Handle: RePEc:ags:uguewp:34129
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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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