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首页> 外文期刊>Journal of the royal statistical society >Are actuarial crop insurance rates fair?: an analysis using a penalized bivariate B-spline method
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Are actuarial crop insurance rates fair?: an analysis using a penalized bivariate B-spline method

机译：精算作物保险费率是否公平？：使用惩罚性双变量B样条方法的分析

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摘要

In this paper, we investigate whether the yield insurance premium rates given by the US Department of Agriculture's Risk Management Agency are actuarially fair by comparing the conditional yield density inferred from premium data with the conditional yield density inferred from yield data. A procedure is developed to estimate the conditional yield density by using premium data through partial derivatives of the premium rate function, as fitted by penalized bivariate tensor product B-splines. We study the asymptotic properties of partial derivatives of a penalized bivariate tensor product B-spline estimator and provide variance estimates. The conditional yield density inferred from premium data and its variance estimator are evaluated through simulation studies. The procedure is also applied to a crop insurance data set from the state of Iowa to examine the actuarial fairness of the premium rates. On average, premium rates are close to our estimates and this is true for each coverage level. However, premiums for low productivity land are generally too low whereas those for high productivity land are generally too high. Even after subsidies, premiums for the more productive land are generally substantially higher than are our estimates of the corresponding actuarially fair rates.
机译：在本文中，我们通过比较从保费数据推断出的条件收益率密度与从收益数据推断出的条件收益率密度，来研究美国农业部风险管理机构给出的收益保险费率在精算上是否公平。开发了一种程序，通过溢价率函数的偏导数使用溢价数据，使用溢价数据来估计条件产量密度，该函数由惩罚双变量张量积B样条拟合。我们研究了惩罚双变量张量积B样条估计的偏导数的渐近性质，并提供了方差估计。通过模拟研究评估了根据溢价数据及其方差估计量得出的条件产量密度。该程序还应用于爱荷华州的农作物保险数据集，以检查保费率的精算公平性。平均而言，保费率接近我们的估计，这对于每个保险水平都是正确的。但是，低生产率土地的保险费通常太低，而高生产率土地的保险费通常太高。即使获得补贴，生产性更高的土地的溢价通常也大大高于我们对相应精算公允汇率的估计。

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