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

Published: 13-07-2021| Version 1 | DOI: 10.17632/jycfnxxysp.1
Daniel Lima Miquelluti,


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.