Next Article in Journal
Forecasting Inflation Uncertainty in the G7 Countries
Next Article in Special Issue
A Hybrid MCMC Sampler for Unconditional Quantile Based on Influence Function
Previous Article in Journal / Special Issue
Using the GB2 Income Distribution
Article Menu

Export Article

Open AccessArticle
Econometrics 2018, 6(2), 22;

Parametric Inference for Index Functionals

Department of Statistics & Institute for CyberScience, Eberly College of Science, Pennsylvania State University, University Park, 16802 PA, USA
Research Center for Statistics, Geneva School of Economics and Management, University of Geneva, 1202 Geneva, Switzerland
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [312 KB, uploaded 3 May 2018]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Guerrier, S.; Orso, S.; Victoria-Feser, M.-P. Parametric Inference for Index Functionals. Econometrics 2018, 6, 22.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Econometrics EISSN 2225-1146 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top