Evaluating yield models for crop insurance rating

Yield distributions;Crop Insurance;Weibull Distribution;Beta Distribution;Mixture Distribution;Burr XII Distribution;Out-of-Sample Efficiency;Goodness-of-Fit;Insurance Rating Efficiency

Lanoue, Christopher ; Sherrick ; Bruce J.

关键词: Yield distributions;    Crop Insurance;    Weibull Distribution;    Beta Distribution;    Mixture Distribution;    Burr XII Distribution;    Out-of-Sample Efficiency;    Goodness-of-Fit;    Insurance Rating Efficiency;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/16920/1_Lanoue_Christopher.pdf?sequence=3&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Crop insurance performance and loss rates depend directly on underlying crop yield distributions. However, there still exists much debate about how to represent the underlying crop yield distributions. Using farm-level corn and soybean yields from 1972-2008, this study examines in-sample goodness-of-fit measures of both the whole distribution and the insurance tail to compare a set of flexible parametric, semi-parametric, and non-parametric distributions in a meaningful economic context. Simulations are then conducted to investigate the out-of-sample efficiency properties of several competing distributions. The results indicate that more parameterized distributional forms fit the data better in-sample, but are generally less efficient out-of-sample - and in some cases more biased - than more parsimonious forms which also fit the data adequately, such as the Weibull. The results highlight the relative advantages of alternative distributions, in terms of the bias-efficiency tradeoff in both in- and out-of-sample frameworks.