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Computational Statistics

, Volume 34, Issue 1, pp 253–279 | Cite as

Approximate Bayesian computation for Lorenz curves from grouped data

  • Genya KobayashiEmail author
  • Kazuhiko Kakamu
Original Paper
  • 90 Downloads

Abstract

This paper proposes a new Bayesian approach to estimate the Gini coefficient from the grouped data on the Lorenz curve. The proposed approach assumes a hypothetical income distribution and estimates the parameter by directly working on the likelihood function implied by the Lorenz curve of the income distribution from the grouped data. It inherits the advantages of two existing approaches through which the Gini coefficient can be estimated more accurately and a straightforward interpretation about the underlying income distribution is provided. Since the likelihood function is implicitly defined, the approximate Bayesian computational approach based on the sequential Monte Carlo method is adopted. The usefulness of the proposed approach is illustrated through the simulation study and the Japanese income data.

Keywords

Generalised beta distribution Gini coefficient Income distribution Sequential Monte Carlo 

Supplementary material

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180_2018_831_MOESM4_ESM.pdf (360 kb)
Supplementary material 4 (pdf 360 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of Social SciencesChiba UniversityChibaJapan
  2. 2.Graduate School of Business AdministrationKobe UniversityKobeJapan

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