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Information entropy method of crop yield distributions: Implications for pricing crop insurance contracts

机译:作物单产分布的信息熵方法:对作物保险合同定价的启示

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Crop yield distributions are the basis of premium rating of agricultural insurance. In this paper, we use maximum entropy density estimation procedures to evaluate crop yield distributions. The procedures are applied to a data set of crop yields for rice, corn, soybean and peanut, for the period of 1980 through 2010 Liaoning province in China. Meanwhile, based on the maximum entropy distributions, implications for rating crop insurance contracts are discussed. Further, all other alternative premium rates are calculated and compared with the maximum entropy ones. The results show that the maximum entropy optimization model can consider more information about crop yield distributions. So, in the situation of limited information, the maximum entropy premium rates are alternative. This study provides a more reasonable method of the determination of agricultural insurance rate which is helpful to the scientific decision-making of agricultural risk.
机译:作物单产分布是农业保险保费等级的基础。在本文中,我们使用最大熵密度估计程序来评估作物产量分布。该程序应用于1980年至2010年中国辽宁省的水稻,玉米,大豆和花生的农作物产量数据集。同时,基于最大熵分布,讨论了对作物保险合同评级的含义。此外,计算所有其他替代保费率,并将其与最大熵保费率进行比较。结果表明,最大熵优化模型可以考虑作物产量分布的更多信息。因此,在信息有限的情况下,可以选择最大熵溢价率。该研究为农业保险费率的确定提供了一种较为合理的方法,有助于科学地确定农业风险。

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