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摘要翻译：
本文研究了当观测量超过一定阈值时，尾特征的估计和推断问题。我们首先表明，忽略这种尾部截尾可能会导致严重的偏差和大小失真，即使截尾概率很小。其次，基于Pareto尾近似提出了一种新的极大似然估计(MLE)，并给出了它的渐近性质。第三，利用极值理论对极大似然方程进行了小样本修正。Monte Carlo仿真表明，改进后的MLE具有优良的小样本性能。我们利用当前人口调查数据集估计了美国个人收入的尾部指数和极端分位数，利用Barro和Urs{\'u}a(2008)收集的数据集估计了宏观经济灾害分布的尾部指数和风险规避系数，从而说明了它的经验相关性。我们新的经验发现与现有的文献有很大的不同。
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英文标题：
《Estimation and Inference about Tail Features with Tail Censored Data》
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作者：
Yulong Wang and Zhijie Xiao
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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{\'u}a (2008). Our new empirical findings are substantially different from the existing literature.
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PDF链接：
https://arxiv.org/pdf/2002.09982
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