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Parametric Inference for Index Functionals

1
Department of Statistics & Institute for CyberScience, Eberly College of Science, Pennsylvania State University, University Park, 16802 PA, USA
2
Research Center for Statistics, Geneva School of Economics and Management, University of Geneva, 1202 Geneva, Switzerland
*
Author to whom correspondence should be addressed.
Econometrics 2018, 6(2), 22; https://doi.org/10.3390/econometrics6020022
Received: 13 December 2017 / Revised: 25 March 2018 / Accepted: 13 April 2018 / Published: 20 April 2018
(This article belongs to the Special Issue Econometrics and Income Inequality)
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. View Full-Text
Keywords: parametric bootstrap; generalized method of moments; income distribution; inequality measurement; heavy tail parametric bootstrap; generalized method of moments; income distribution; inequality measurement; heavy tail
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Guerrier, S.; Orso, S.; Victoria-Feser, M.-P. Parametric Inference for Index Functionals. Econometrics 2018, 6, 22.

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