Estimation and Inference about Tail Features with Tail Censored Data

Yulong Wang and Zhijie Xiao

No 994, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: 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 Ursúa (2008). Our new empirical findings are substantially different from the existing literature.

Keywords: Extreme Value theory; power law; extreme quantile; tail index (search for similar items in EconPapers)
JEL-codes: C4 (search for similar items in EconPapers)
Date: 2020-03-20
New Economics Papers: this item is included in nep-ore and nep-rmg
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Related works:
Journal Article: Estimation and inference about tail features with tail censored data (2022)
Working Paper: Estimation and Inference about Tail Features with Tail Censored Data (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:994

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