An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | RAC. Revista de Administração Contemporânea (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-65552022000301104 |
Resumo: | ABSTRACT 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 DesignGWQlassoindex insurancesystemic riskcrop insuranceABSTRACT 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.Associação Nacional de Pós-Graduação e Pesquisa em Administração2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-65552022000301104Revista de Administração Contemporânea v.26 n.3 2022reponame:RAC. Revista de Administração Contemporânea (Online)instname:Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD)instacron:ANPAD10.1590/1982-7849rac2022200387.eninfo:eu-repo/semantics/openAccessMiquelluti,Daniel LimaOzaki,Vitor AugustoMiquelluti,David Joséeng2022-01-19T00:00:00Zoai:scielo:S1415-65552022000301104Revistahttps://rac.anpad.org.br/index.php/racONGhttps://rac.anpad.org.br/index.php/rac/oairac@anpad.org.br1982-78491415-6555opendoar:2022-01-19T00:00RAC. Revista de Administração Contemporânea (Online) - Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD)false |
dc.title.none.fl_str_mv |
An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design |
title |
An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design |
spellingShingle |
An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design Miquelluti,Daniel Lima GWQlasso index insurance systemic risk crop insurance |
title_short |
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_sort |
An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design |
author |
Miquelluti,Daniel Lima |
author_facet |
Miquelluti,Daniel Lima Ozaki,Vitor Augusto Miquelluti,David José |
author_role |
author |
author2 |
Ozaki,Vitor Augusto Miquelluti,David José |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Miquelluti,Daniel Lima Ozaki,Vitor Augusto Miquelluti,David José |
dc.subject.por.fl_str_mv |
GWQlasso index insurance systemic risk crop insurance |
topic |
GWQlasso index insurance systemic risk crop insurance |
description |
ABSTRACT 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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-65552022000301104 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-65552022000301104 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1982-7849rac2022200387.en |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Nacional de Pós-Graduação e Pesquisa em Administração |
publisher.none.fl_str_mv |
Associação Nacional de Pós-Graduação e Pesquisa em Administração |
dc.source.none.fl_str_mv |
Revista de Administração Contemporânea v.26 n.3 2022 reponame:RAC. Revista de Administração Contemporânea (Online) instname:Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD) instacron:ANPAD |
instname_str |
Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD) |
instacron_str |
ANPAD |
institution |
ANPAD |
reponame_str |
RAC. Revista de Administração Contemporânea (Online) |
collection |
RAC. Revista de Administração Contemporânea (Online) |
repository.name.fl_str_mv |
RAC. Revista de Administração Contemporânea (Online) - Associação Nacional de Pós-Graduação e Pesquisa em Administração (ANPAD) |
repository.mail.fl_str_mv |
rac@anpad.org.br |
_version_ |
1754209054104748032 |