Statistical Inference with Generalized Gini Indices of Inequality, Poverty, and Welfare
提出一种无需将人口分组为固定分位数的广义基尼指数估计方法，证明其渐近正态性，并通过模拟和美国收入数据比较了不同推断方法，发现基于渐近理论的bootstrap-t方法在小样本中表现更优。
This article considers statistical inference for consistent estimators of generalized Gini indices of inequality, poverty, and welfare. Our method does not require grouping the population into a fixed number of quantiles. The empirical indices are shown to be asymptotically normally distributed using functional limit theory. Easily computed asymptotic variance expressions are obtained using influence functions. Inference based on first-order asymptotics is then compared with the grouped method and various bootstrap methods in simulations and with U.S. income data. The bootstrap-t method based on our asymptotic theory is found to have superior size and power properties in small samples.