A semiparametric spatio‐temporal model of crop yield trend and its implication to insurance rating
提出一个半参数时空趋势模型，用两节点样条函数拟合时间趋势、径向基函数处理空间变化系数，并通过滚动回归验证优化平滑参数，实证表明该模型在识别盈利保险保单上优于其他模型。
Abstract We demonstrate the benefit of spatial smoothing for crop trend estimation with a deterministic spatio‐temporal trend model. The proposed model is semiparametric, where the parametric temporal trend is modeled with a two‐knot spline function for forecasting robustness, and the nonparametric spatially‐varying coefficients are modeled by the radial basis function method for flexibility. To select the smoothing parameter of our trend model, we propose a forward validation criterion tailored to meet the forecasting nature of rating crop insurance. This criterion is based on a rolling regression approach that adds one year of data at a time for validation. We also propose a new criterion for model comparison using relative mean squared error in forecasting insurance payouts. Our empirical results show that the proposed trend model is more efficient and capable of identifying profitable insurance policies than two competing models in most state‐crop combinations.