Inequality measurement with grouped data: Parametric and non-parametric methods

被引:7
|
作者
Jorda, Vanesa [1 ]
Sarabia, Jose Maria [2 ]
Jantti, Markus [3 ]
机构
[1] Univ Cantabria, Santander, Spain
[2] CUNEF Univ, Madrid, Spain
[3] Stockholm Univ, Stockholm, Sweden
关键词
generalised beta distribution of the second kind; kernel density estimator; Lorenz curve; minimum distance estimators; KERNEL DENSITY-ESTIMATION; GLOBAL INCOME-DISTRIBUTION; LARGE-SAMPLE PROPERTIES; SIZE DISTRIBUTION; PERSONAL INCOME; GMM ESTIMATION; POVERTY; DISTRIBUTIONS; INFERENCE; INDEXES;
D O I
10.1111/rssa.12702
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
Grouped data in the form of income shares have conventionally been used to estimate income inequality due to the lack of individual records. We present a systematic evaluation of the performance of parametric distributions and non-parametric techniques to estimate economic inequality using more than 3300 data sets. We also provide guidance on the choice between these two approaches and their estimation, for which we develop the GB2group R package. Our results indicate that even the simplest parametric models provide reliable estimates of inequality measures. The non-parametric approach, however, fails to represent income distributions accurately.
引用
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页码:964 / 984
页数:21
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