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Unified smoothed jackknife empirical likelihood tests for comparing income inequality indices
Statistical Papers ( IF 1.3 ) Pub Date : 2022-01-25 , DOI: 10.1007/s00362-021-01281-w
Yang Wei 1 , Zhouping Li 1 , Yunqiu Dai 1
Affiliation  

In the economic and social development, income inequality is an important issue. To measure the income inequality or poverty, many economic indices were introduced in the literature, including the Gini index, Bonferroni index and De Vergottini index, etc. Inference approaches to these indices have been studied extensively in the past decades. By noting that these indices can be written in a unified integral form of the weighted Lorenz curve, this paper develops a smoothed jackknife empirical likelihood (EL) method to make inferences on the difference between indices in a unified framework. Under some mild conditions, we derive the asymptotic distribution of the log EL ratio statistic. Moreover, we carry out extensive Monte Carlo simulation studies and real data analysis to illustrate the performance of the proposed approach.



中文翻译:

用于比较收入不平等指数的统一平滑折刀经验似然检验

在经济社会发展中,收入不平等是一个重要问题。为了衡量收入不平等或贫困,文献中引入了许多经济指标,包括基尼指数、Bonferroni 指数和 De Vergottini 指数等。这些指数的推理方法在过去几十年中得到了广泛的研究。通过注意到这些指标可以写成加权洛伦兹曲线的统一积分形式,本文开发了一种平滑折刀经验似然(EL)方法,以在统一框架中推断指标之间的差异。在一些温和的条件下,我们推导出 log EL ratio 统计量的渐近分布。此外,我们进行了广泛的蒙特卡罗模拟研究和真实数据分析,以说明所提出方法的性能。

更新日期:2022-01-25
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