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Empirical likelihood confidence intervals for the Gini measure of income inequality

Author

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  • Qin, Yongsong
  • Rao, J.N.K.
  • Wu, Changbao

Abstract

Gini coefficient is among the most popular and widely used measures of income inequality in economic studies, with various extensions and applications in finance and other related areas. This paper studies confidence intervals on the Gini coefficient for simple random samples, using normal approximation, bootstrap percentile, bootstrap-t and the empirical likelihood method. Through both theory and simulation studies it is shown that the intervals based on normal or bootstrap approximation are less satisfactory for samples of small or moderate size than the bootstrap-calibrated empirical likelihood ratio confidence intervals which perform well for all sample sizes. Results for stratified random sampling are also presented.

Suggested Citation

  • Qin, Yongsong & Rao, J.N.K. & Wu, Changbao, 2010. "Empirical likelihood confidence intervals for the Gini measure of income inequality," Economic Modelling, Elsevier, vol. 27(6), pages 1429-1435, November.
  • Handle: RePEc:eee:ecmode:v:27:y:2010:i:6:p:1429-1435
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    References listed on IDEAS

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    6. Jing, Bing-Yi & Yuan, Junqing & Zhou, Wang, 2008. "Empirical likelihood for non-degenerate U-statistics," Statistics & Probability Letters, Elsevier, vol. 78(6), pages 599-607, April.
    7. Sandstrom, Arne & Wretman, Jan H & Walden, Bertil, 1988. "Variance Estimators of the Gini Coefficient--Probability Sampling," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 113-119, January.
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    Cited by:

    1. Lv, Xiaofeng & Li, Rui & Fang, Zheng, 2017. "Efficient semiparametric estimation for Gini inequality treatment effects," Economics Letters, Elsevier, vol. 154(C), pages 96-100.
    2. repec:kap:jecinq:v:15:y:2017:i:2:d:10.1007_s10888-017-9348-8 is not listed on IDEAS
    3. Wang, Dongliang & Zhao, Yichuan & Gilmore, Dirk W., 2016. "Jackknife empirical likelihood confidence interval for the Gini index," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 289-295.
    4. Xiaofeng Lv & Gupeng Zhang & Xinkuo Xu & Qinghai Li, 0. "Bootstrap-calibrated empirical likelihood confidence intervals for the difference between two Gini indexes," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 0, pages 1-22.
    5. Judith A. Clarke & Ahmed A. Hoque, 2014. "On Variance Estimation for a Gini Coefficient Estimator Obtained from Complex Survey Data," Econometrics Working Papers 1401, Department of Economics, University of Victoria.
    6. Tsao, Min & Wu, Fan, 2015. "Two-sample extended empirical likelihood for estimating equations," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 1-15.
    7. Xiaofeng Lv & Gupeng Zhang & Guangyu Ren, 2017. "Gini index estimation for lifetime data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 275-304, April.

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