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Statistical inference for inequality and poverty measurement with depenent data

机译:利用相关数据对不平等和贫困程度进行统计推断

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This article is about statistical inference for inequality and poverty measures when income data exhibit contemporaneous dependence across members of the same household. While much empirical research is based on household survey data such as the PSID, standard methods assume that income is an independent and identically distributed random variable. Applying them to contemporane- ously dependent data produces biased results, and Monte Carlo experiments reveal that their confidence intervals are too narrow. By contrast, our proposed distribution-free estimators perform well.
机译:本文是关于当收入数据在同一家庭成员中同时依赖时的不平等和贫困测度的统计推断。尽管许多实证研究都是基于家庭调查数据(例如PSID),但标准方法假设收入是一个独立且分布均匀的随机变量。将它们应用于同时依赖的数据会产生偏差的结果,蒙特卡洛实验表明它们的置信区间太窄。相比之下,我们提出的无分布估算器表现良好。

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