Peer review report for: An application of geographically weighted quantile lasso to weather index insurance design

Miquelluti, Daniel Lima; Ozaki, Vitor Augusto; Miquelluti, David José; Júnior, Paulo Hubert

doi:10.5281/zenodo.5728233

2021

DOI: 10.5281/zenodo.5728233

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Peer review report for: An application of geographically weighted quantile lasso to weather index insurance design

Daniel Lima Miquelluti

Vitor Augusto Ozaki

David José Miquelluti

et al.

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

2022

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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.

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

2022

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Peer review report for: An application of geographically weighted quantile lasso to weather index insurance design

Cited by 1 publication

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

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

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