Data for: An application of geographically weighted quantile LASSO to weather index insurance design published by RAC-Revista de Administração Contemporânea

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 a 1-month standardized precipitation index (SPI) derived from a daily precipitation dataset for 41 weather stations in the State of Paraná (Brazil) for the period of 1979 through 2015. Soybean yield data are also used for the 41 municipalities from 1980 through 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. About the datasets: Daily precipitation (mm) and yearly soybean crop yields (kg/ha) at the municipality level in Brazil. The precise location of each weather station is also present in the precipitation dataset.研究目标：本文针对天气指数保险产品的产量-指数关系建模问题，探究一种新型回归方法——地理加权分位数LASSO（geographically weighted quantile LASSO, GWQLASSO）的建模效能。该方法允许回归系数随空间位置变化，同时可借助邻近站点的信息生成稳健估计结果；模型中的LASSO模块可实现相关解释变量的筛选。 研究方法：本文基于1979年至2015年巴西巴拉那州41个气象站的日降水量数据集，构建了以1个月尺度标准化降水指数（standardized precipitation index, SPI）为核心的天气指数保险（weather index insurance, WII）产品。同时采用了1980年至2015年上述41个市镇的大豆产量数据。本文采用谱风险测度（Spectral Risk Measure, SRM）与平均半离差，将GWQLASSO方法的建模效果与经典分位数回归方法、传统产量保险产品进行对比评估。 研究结果：尽管GWQLASSO与经典分位数回归方法的建模效果相当，但其表现优于传统产量保险产品。 研究结论：综上，GWQLASSO可作为巴西及其他数据有限地区的作物保险市场的可选建模方案。 数据集说明：包含巴西各市政辖区层面的日降水量（单位：毫米）与年度大豆作物产量（单位：千克/公顷）；降水数据集中同时标注了各气象站的精确地理位置。