The University of Southampton
University of Southampton Institutional Repository

On the problem of inference for inequality measures for heavy-tailed distributions

Schluter, Christian (2012) On the problem of inference for inequality measures for heavy-tailed distributions Econometrics Journal, 15, (1), pp. 125-153. (doi:10.1111/j.1368-423X.2011.00356.x).

Record type: Article

Abstract

We consider the class of heavy-tailed income distributions and show that the shape of the income distribution has a strong effect on inference for inequality measures. In particular, we demonstrate how the severity of the inference problem responds to the exact nature of the right tail of the income distribution. It is shown that the density of the studentised inequality measure is heavily skewed to the left, and that the excessive coverage failures of the usual confidence intervals are associated with excessively low estimates of both the point measure and the variance. For further diagnostics, the coefficients of bias, skewness and kurtosis are derived and examined for both studentised and standardised inequality measures. These coefficients are also used to correct the size of confidence intervals. Exploiting the uncovered systematic relationship between the inequality estimate and its estimated variance, variance stabilising transforms are proposed and shown to improve inference significantly.

Full text not available from this repository.

More information

e-pub ahead of print date: 16 February 2012
Published date: February 2012
Keywords: inequality measures, inference, statistical performance, asymptotic expansions, variance stabilisation
Organisations: Economics

Identifiers

Local EPrints ID: 154445
URI: http://eprints.soton.ac.uk/id/eprint/154445
ISSN: 1368-4221
PURE UUID: 898f5b8d-969d-4eb7-a933-60828ee35570

Catalogue record

Date deposited: 25 May 2010 14:39
Last modified: 18 Jul 2017 12:47

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×