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 has proved valuable as a measure of income inequality. In cross-sectional studies of the Gini coefficient, information about the accuracy of its estimate 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 generalised jackknife estimator and the Taylor linearization estimator for the variance of the Gini coefficient. A brief simulation study supports our findings.
University of Southampton
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
9 May 2008
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
(S3RI Methodology Working Papers, M08/04)
Southampton, GB.
University of Southampton
22pp.
Record type:
Monograph
(Working Paper)
Abstract
The Gini coefficient has proved valuable as a measure of income inequality. In cross-sectional studies of the Gini coefficient, information about the accuracy of its estimate 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 generalised jackknife estimator and the Taylor linearization estimator for the variance of the Gini coefficient. A brief simulation study supports our findings.
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51201-01.pdf
- Author's Original
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Published date: 9 May 2008
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 51201
URI: http://eprints.soton.ac.uk/id/eprint/51201
PURE UUID: ae7a890f-fa65-4199-b942-73d383cc7d38
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Date deposited: 09 May 2008
Last modified: 16 Mar 2024 03:03
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