论文标题
估计和推断尾部特征和尾部审查数据
Estimation and Inference about Tail Features with Tail Censored Data
论文作者
论文摘要
本文考虑了超出某个阈值的观察值时，考虑了有关尾部特征的估计和推断。我们首先表明，即使审查概率很小，忽略这种尾巴检查可能会导致巨大的偏见和大小失真。其次，我们根据帕累托尾近似提出了一个新的最大似然估计量（MLE），并得出其渐近性能。第三，我们通过诉诸极值理论为MLE提供了少量的样本修改。如Monte Carlo模拟所示，具有这种修饰的MLE可提供出色的小样本性能。我们通过估算（i）使用当前人口调查数据集的尾部指数和美国个人收入的极端分位数来说明其经验相关性，以及（ii）使用Barro和URS {2008} A（2008）收集的宏观经济灾害分布的尾部索引以及宏观经济灾害分布的尾部指数以及使用风险避免系数。我们的新经验发现与现有文献大不相同。
This paper considers estimation and inference about tail features when the observations beyond some threshold are censored. We first show that ignoring such tail censoring could lead to substantial bias and size distortion, even if the censored probability is tiny. Second, we propose a new maximum likelihood estimator (MLE) based on the Pareto tail approximation and derive its asymptotic properties. Third, we provide a small sample modification to the MLE by resorting to Extreme Value theory. The MLE with this modification delivers excellent small sample performance, as shown by Monte Carlo simulations. We illustrate its empirical relevance by estimating (i) the tail index and the extreme quantiles of the US individual earnings with the Current Population Survey dataset and (ii) the tail index of the distribution of macroeconomic disasters and the coefficient of risk aversion using the dataset collected by Barro and Urs{ú}a (2008). Our new empirical findings are substantially different from the existing literature.
