ASYMPTOTIC AND BOOTSTRAP INFERENCE FOR INEQUALITY AND POVERTY MEASURES

Russell Davidson and Emmanuel Flachaire

Departmental Working Papers from McGill University, Department of Economics

Abstract: A random sample drawn from a population would appear to offer an ideal opportunity to use the bootstrap in order to perform accurate inference, since the observations of the sample are IID. In this paper, Monte Carlo results suggest that bootstrapping a commonly used index of inequality leads to inference that is not accurate even in very large samples,although inference with poverty indices is satisfactory. We find that the major cause is the extreme sensitivity of many inequality indices to the exact nature of the upper tail of the income distribution. This leads us to study two non-standard bootstraps, the m out of n bootstrap, which is valid in some situations where the standard bootstrap fails, and a bootstrap in which the upper tail is modelled parametrically. Monte Carlo results suggest that accurate inference can be achieved with this last method in moderately large samples.

JEL-codes: C14 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2006-09
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Asymptotic and bootstrap inference for inequality and poverty measures (2007)
Working Paper: Asymptotic and bootstrap inference for inequality and poverty measures (2007)
Working Paper: Asymptotic and bootstrap inference for inequality and poverty measures (2007)
Working Paper: Asymptotic and bootstrap inference for inequality and poverty measures (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:mcl:mclwop:2005-06

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