Estimation of upper and lower bounds of Gini coefficient by fuzzy data

The data presented in this paper are used to examine the uncertainty in macroeconomic variables and their impact on the Gini coefficient. Annual data for the period 2017 - 1996 are taken from the Bank of Iran website https://www.cbi.ir. We used fuzzy regression with symmetric coefficients to calcula...

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Detalles Bibliográficos
Autores principales: Ganjoei, Reza Ashraf, Akbarifard, Hossein, Mashinchi, Mashaallah, Majid Jalaee Esfandabadi, Sayyed Abdol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Computer Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038473/
https://www.ncbi.nlm.nih.gov/pubmed/32123714
http://dx.doi.org/10.1016/j.dib.2020.105288
Descripción
Sumario:The data presented in this paper are used to examine the uncertainty in macroeconomic variables and their impact on the Gini coefficient. Annual data for the period 2017 - 1996 are taken from the Bank of Iran website https://www.cbi.ir. We used fuzzy regression with symmetric coefficients to calculate upper and lower bound data of Gini coefficient. Estimated data at this stage can be a very useful guide for policymakers, on the other hand, it is a benchmark for evaluating the effectiveness of government policies. The reason for using fuzzy regression to estimate data on Gini coefficients is the extra flexibility of this model.

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