Single Imputation Methods and Confidence Intervals for the Gini Index

被引:1
|
作者
Alvarez-Verdejo, Encarnacion [1 ]
Moya-Fernandez, Pablo J. [1 ]
Munoz-Rosas, Juan Francisco F. [1 ]
机构
[1] Univ Granada, Dept Quantitat Methods Econ & Business, Granada 18011, Spain
关键词
missing data; variance estimation; coverage; inequality; non-response mechanism; VARIANCE-ESTIMATION; INCOME INEQUALITY; INFERENCE; JACKKNIFE; COEFFICIENT; POVERTY; EUROPE; HEALTH;
D O I
10.3390/math9243252
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The problem of missing data is a common feature in any study, and a single imputation method is often applied to deal with this problem. The first contribution of this paper is to analyse the empirical performance of some traditional single imputation methods when they are applied to the estimation of the Gini index, a popular measure of inequality used in many studies. Various methods for constructing confidence intervals for the Gini index are also empirically evaluated. We consider several empirical measures to analyse the performance of estimators and confidence intervals, allowing us to quantify the magnitude of the non-response bias problem. We find extremely large biases under certain non-response mechanisms, and this problem gets noticeably worse as the proportion of missing data increases. For a large correlation coefficient between the target and auxiliary variables, the regression imputation method may notably mitigate this bias problem, yielding appropriate mean square errors. We also find that confidence intervals have poor coverage rates when the probability of data being missing is not uniform, and that the regression imputation method substantially improves the handling of this problem as the correlation coefficient increases.
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收藏
页数:20
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