An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design
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 informati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD)
2021-10-01
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| Series: | RAC: Revista de Administração Contemporânea |
| Subjects: | |
| Online Access: | https://rac.anpad.org.br/index.php/rac/article/view/1506 |
| _version_ | 1797714561822883840 |
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| author | Daniel Lima Miquelluti Vitor Augusto Ozaki David José Miquelluti |
| author_facet | Daniel Lima Miquelluti Vitor Augusto Ozaki David José Miquelluti |
| author_sort | Daniel Lima Miquelluti |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T07:53:47Z |
| format | Article |
| id | doaj.art-440905111dab4dcc89540c03b42a2d32 |
| institution | Directory Open Access Journal |
| issn | 1415-6555 1982-7849 |
| language | English |
| last_indexed | 2024-03-12T07:53:47Z |
| publishDate | 2021-10-01 |
| publisher | Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD) |
| record_format | Article |
| series | RAC: Revista de Administração Contemporânea |
| spelling | doaj.art-440905111dab4dcc89540c03b42a2d322023-09-02T20:22:45ZengAssociação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD)RAC: Revista de Administração Contemporânea1415-65551982-78492021-10-01263e200387e20038710.1590/1982-7849rac2022200387.en1506An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance DesignDaniel Lima Miquelluti0Vitor Augusto Ozaki1David José Miquelluti2Universidade de São Paulo, Escola Superior de Agricultura "Luiz de Queiroz", Departamento de Economia, Administração e Sociologia, Piracicaba, São Paulo, Brazil. Universidade de São Paulo, Escola Superior de Agricultura "Luiz de Queiroz", Departamento de Economia, Administração e Sociologia, Piracicaba, São Paulo, Brazil. Universidade do Estado de Santa Catarina, Centro de Ciências Agroveterinárias, Departamento de Solos e Recursos Naturais, Lages, SC, Brazil.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.https://rac.anpad.org.br/index.php/rac/article/view/1506gwqlassoindex insurancesystemic riskcrop insurance |
| spellingShingle | Daniel Lima Miquelluti Vitor Augusto Ozaki David José Miquelluti An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design RAC: Revista de Administração Contemporânea gwqlasso index insurance systemic risk crop insurance |
| title | An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design |
| title_full | An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design |
| title_fullStr | An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design |
| title_full_unstemmed | An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design |
| title_short | An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design |
| title_sort | application of geographically weighted quantile lasso to weather index insurance design |
| topic | gwqlasso index insurance systemic risk crop insurance |
| url | https://rac.anpad.org.br/index.php/rac/article/view/1506 |
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