Replication 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：本研究旨在探究一种新型回归方法——地理加权分位数LASSO（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能。GWQLASSO允许回归系数随空间维度变化，同时借助邻近站点的信息生成稳健可靠的估计值；该模型的LASSO模块可辅助筛选关键解释变量。
研究方法：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，构建1个月尺度的标准化降水指数（Standardized Precipitation Index，SPI），并以此开发一款天气指数保险（Weather Index Insurance，WII）产品。同时采用1980年至2015年上述41个对应市镇的大豆产量数据。本研究采用谱风险测度（Spectral Risk Measure，SRM）与平均半离差，将GWQLASSO模型的效果与经典分位数回归方法及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效能相当，但其表现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的作物保险市场的备选方案。
数据集说明：本数据集包含巴西各市级行政区的日降水量（单位：毫米）与年度大豆作物产量（单位：千克/公顷），且降水数据集中标注了每个气象站的精确地理位置。