Parametric Inference for Index Functionals

Stéphane Guerrier, Samuel Orso and Maria-Pia Victoria-Feser ()
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Stéphane Guerrier: Department of Statistics & Institute for CyberScience, Eberly College of Science, Pennsylvania State University, University Park, 16802 PA, USA
Samuel Orso: Research Center for Statistics, Geneva School of Economics and Management, University of Geneva, 1202 Geneva, Switzerland

Econometrics, 2018, vol. 6, issue 2, 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.

Keywords: parametric bootstrap; generalized method of moments; income distribution; inequality measurement; heavy tail (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2018
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