An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design

ABSTRACT Objective: this article studies the efficiency of a novel regression approach, the geographically weighted quantile lasso (GWQlasso), in the modeling of yield-index relationship for weather index insurance products. GWQlasso allows regression coefficients to vary spatially, while using the information from neighboring locations to derive robust estimates. The lasso component of the model facilitates the selection of relevant explanatory variables. Methodology: a weather index insurance (WII) product is developed based on one-month standardized precipitation index (SPI) derived from a daily precipitation dataset for 41 weather stations in the state of Paraná (Brazil) for the period from 1979 to 2015. Soybean yield data are also used for the 41 municipalities from 1980 to 2015. The effectiveness of the GWQlasso product is evaluated against a classic quantile regression approach and a traditional yield insurance product using the spectral risk measure (SRM) and the mean semi-deviation. Results: while GWQlasso proved as effective as quantile regression, it outperformed the yield insurance product. Conclusion: the GWQlasso is an alternative to the crop insurance market in Brazil and other locations with limited data.摘要 研究目的：本文探究一种新型回归方法——地理加权分位数套索（geographically weighted quantile lasso，GWQlasso）在天气指数保险产品的产量-指数关系建模中的应用效率。GWQlasso允许回归系数随空间变化，并可利用邻近站点的信息生成稳健估计值。该模型的套索组件可辅助筛选相关解释变量。 研究方法：本研究基于1979年至2015年巴西巴拉那州41个气象站的日降水数据集，计算得到月尺度标准化降水指数（standardized precipitation index，SPI），并以此构建天气指数保险（weather index insurance，WII）产品。同时采用1980年至2015年该州41个市镇的大豆产量数据。以谱风险测度（spectral risk measure，SRM）与平均半离差为评估指标，将GWQlasso方法的有效性与经典分位数回归方法及传统产量保险产品进行对比。 研究结果：尽管GWQlasso的表现与分位数回归相当，但它优于传统产量保险产品。 研究结论：GWQlasso可作为巴西及其他数据有限地区作物保险市场的备选方案。