Parametric inference for index functionals

Stéphane Guerrier, Samuel Orso, Maria Pia Victoria-Feser

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number22
JournalEconometrics
Volume6
Issue number2
DOIs
StatePublished - Jun 2018

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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