Pareto指数与最高收入份额的有效最小距离估计
Efficient Minimum Distance Estimation of Pareto Exponent from Top Income Shares
摘要：
我们提出了一个有效的估计方法的收入帕累托指数时，只有某些最高收入的份额是可观察的。我们的估计是基于顺序统计量的加权和的渐近理论和有效的最小距离估计。仿真结果表明，我们的估计具有良好的有限样本性质。我们将我们的估计方法应用于美国最高收入份额数据，发现帕累托指数自1985年以来一直稳定在1.5左右，这表明过去三十年来不平等的加剧主要是由富人和穷人之间的再分配驱动的，而不是富人之间的再分配。
英文摘要：
We propose an efficient estimation method for the income Pareto exponent when only certain top income shares are observable. Our estimator is based on the asymptotic theory of weighted sums of order statistics and the efficient minimum distance estimator. Simulations show that our estimator has excellent finite sample properties. We apply our estimation method to U.S. top income share data and find that the Pareto exponent has been stable at around 1.5 since 1985, suggesting that the rise in inequality during the last three decades is mainly driven by redistribution between the rich and poor, not among the rich.
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