Recovering income distribution in the presence of interval-censored data

Fernando Rios-Avila, Gustavo Canavire-Bacarreza, Flavia Sacco-Capurro
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引用次数: 0

Abstract

We propose a method to analyze interval-censored data using a multiple imputation based on a Heteroskedastic Interval regression approach. The proposed model aims to obtain a synthetic dataset that can be used for standard analysis, including standard linear regression, quantile regression, or poverty and inequality estimation. We present two applications to show the performance of our method. First, we run a Monte Carlo simulation to show the method's performance under the assumption of multiplicative heteroskedasticity, with and without conditional normality. Second, we use the proposed methodology to analyze labor income data in Grenada for 2013-2020, where the salary data are interval-censored according to the salary intervals prespecified in the survey questionnaire. The results obtained are consistent across both exercises.

在存在区间删失数据的情况下恢复收入分布
我们提出了一种利用基于异方差区间回归方法的多重估算来分析区间删失数据的方法。所提出的模型旨在获得一个合成数据集,该数据集可用于标准分析,包括标准线性回归、量化回归或贫困与不平等估计。我们提出了两个应用来展示我们方法的性能。首先,我们进行了蒙特卡罗模拟,展示了该方法在有条件正态性和无条件正态性的乘法异方差假设下的性能。其次,我们使用所提出的方法分析了格林纳达 2013-2020 年的劳动收入数据,其中工资数据根据调查问卷中预设的工资区间进行了区间删减。两种方法得出的结果是一致的。
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