Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/195458
Authors: 
Guerrier, Stéphane
Orso, Samuel
Victoria-Feser, Maria-Pia
Year of Publication: 
2018
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 6 [Year:] 2018 [Issue:] 2 [Pages:] 1-11
Abstract: 
In this paper, we study the finite sample accuracy of confidence intervals for index functional built via parametric bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution, we propose a Generalized Method of Moment estimator that targets the quantity of interest, namely the considered inequality index. Its primary advantage is that the scale parameter does not need to be estimated to perform parametric bootstrap, since inequality measures are scale invariant. The very good finite sample coverages that are found in a simulation study suggest that this feature provides an advantage over the parametric bootstrap using the maximum likelihood estimator. We also find that overall, a parametric bootstrap provides more accurate inference than its non or semi-parametric counterparts, especially for heavy tailed income distributions.
Subjects: 
parametric bootstrap
generalized method of moments
income distribution
inequality measurement
heavy tail
JEL: 
C10
C13
C15
C43
C46
D31
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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