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Parametric inference for index functionals

Published in Econometrics. 2018, vol. 6, no. 2, p. 22
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 bootstrapgeneralized method of momentsincome distributioninequality measurementheavy tail
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GUERRIER, Stéphane, ORSO, Samuel, VICTORIA-FESER, Maria-Pia. Parametric inference for index functionals. In: Econometrics, 2018, vol. 6, n° 2, p. 22. doi: 10.3390/econometrics6020022 https://archive-ouverte.unige.ch/unige:104843

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Deposited on : 2018-06-01

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