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A note on the asymptotic equivalence of jackknife and linearization variance estimation for the Gini coefficient

A note on the asymptotic equivalence of jackknife and linearization variance estimation for the Gini coefficient
A note on the asymptotic equivalence of jackknife and linearization variance estimation for the Gini coefficient
The Gini coefficient (Gini 1914) has proved valuable as a measure of income inequality. In cross-sectional studies of the Gini coefficient, information about the accuracy of its estimates is crucial. We show how to use jackknife and linearization to estimate the variance of the Gini coefficient, allowing for the effect of the sampling design. The aim is to show the asymptotic equivalence (or consistency) of the generalized jackknife estimator (Campbell 1980) and the Taylor linearization estimator (Kovac?evic´ and Binder 1997) for the variance of the Gini coefficient. A brief simulation study supports our findings
inclusion probability, linearization, survey weight, sampling design
0282-423X
541-555
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b

Berger, Yves G. (2008) A note on the asymptotic equivalence of jackknife and linearization variance estimation for the Gini coefficient. Journal of Official Statistics, 24 (4), 541-555.

Record type: Article

Abstract

The Gini coefficient (Gini 1914) has proved valuable as a measure of income inequality. In cross-sectional studies of the Gini coefficient, information about the accuracy of its estimates is crucial. We show how to use jackknife and linearization to estimate the variance of the Gini coefficient, allowing for the effect of the sampling design. The aim is to show the asymptotic equivalence (or consistency) of the generalized jackknife estimator (Campbell 1980) and the Taylor linearization estimator (Kovac?evic´ and Binder 1997) for the variance of the Gini coefficient. A brief simulation study supports our findings

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Published date: December 2008
Keywords: inclusion probability, linearization, survey weight, sampling design
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 350399
URI: https://eprints.soton.ac.uk/id/eprint/350399
ISSN: 0282-423X
PURE UUID: 95eca6b6-8bf5-45f0-b779-e11c2aea2d85

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Date deposited: 28 Mar 2013 11:59
Last modified: 23 May 2018 16:37

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