Replication Data for: An application of geographically weighted quantile LASSO to weather index insurance design published by RAC-Revista de Administração Contemporânea
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. 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 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 ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["crop insurance","GWQlasso","index insurance","systemic risk"]} <|eot_id|>
 Here is the Description of a dataset. 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 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 ' . Please return only a json list of at most 3 Keywords corresponding to the 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 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 
crop insurance; GWQlasso; index insurance; systemic risk