Time-varying Yield Distributions and the U.S. Crop Insurance Program-时变产量分布...

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英文文献：Time-varying Yield Distributions and the U.S. Crop Insurance Program-时变产量分布和美国作物保险计划
英文文献作者：Zhu, Ying,Goodwin, Barry K.,Ghosh, Sujit K.
英文文献摘要：
The objective of this study is to evaluate and model the yield risk associated with major agricultural commodities in the U.S. We are particularly concerned with the nonstationary nature of the yield distribution, which primarily arises because of technological progress and changing environmental conditions. Precise risk assessment depends on the accuracy of modeling this distribution. This problem becomes more challenging as the yield distribution changes over time, a condition that holds for nearly all major crops. A common approach to this problem is based on a two-stage method in which the yield is first detrended and then the estimated residuals are treated as observed data and modeled using various parametric or nonparametric methods. We propose an alternative parametric model that allows the moments of the yield distributions to change with time. Several model selection techniques suggest that the proposed time-varying model outperforms more conventional models in terms of in-sample goodness-of-fit, out-of- sample predictive power and the prediction accuracy of insurance premium rates.

本研究的目的是评估和模拟与美国主要农产品相关的产量风险。我们特别关注产量分布的非平稳性质，这主要是由于技术进步和环境条件的变化而产生的。精确的风险评估取决于对这种分布建模的准确性。随着产量分布随时间而变化，这一问题变得更具挑战性，而这种情况几乎适用于所有主要作物。解决这一问题的一种常用方法是基于一种两阶段的方法，在这种方法中，首先将产量去趋势化，然后将估计的残差作为观察数据处理，并使用各种参数或非参数方法建模。我们提出了另一种参数模型，允许产量分布的矩随时间变化。几种模型选择技术表明，时变模型在样本内拟合优度、样本外预测能力和费率预测精度方面优于传统模型。

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