Bootstrapping inequality measures under the null hypothesis: Is it worth the effort?
This paper discusses methods of statistical inference for inequality measures, in particular the nonparametric bootstrap. Standard resampling techniques and a new method for nonparametric resampling under the null hypothesis are discussed. Monte-Carlo simulations show that some bootstrap methods outperform the commonly used normal approximation while other bootstrap methods—including those which are used in most empirical applications—are hardly any better. Copyright Springer-Verlag 2002
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- Heinrich, Georges, 1998. "Ageing Gracefully? A Bootstrap Analysis of Poverty Among Pensioners Using Evidence from the PACO Databases," CEPR Discussion Papers 2039, C.E.P.R. Discussion Papers.
- Jeffrey A. Mills & Sourushe Zandvakili, 1999.
"Statistical Inference via Bootstrapping for Measures of Inequality,"
- Mills, Jeffrey A & Zandvakili, Sourushe, 1997. "Statistical Inference via Bootstrapping for Measures of Inequality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 133-50, March-Apr.
- Biewen, Martin, 2002. "Bootstrap inference for inequality, mobility and poverty measurement," Journal of Econometrics, Elsevier, vol. 108(2), pages 317-342, June.
- Davidson, Russell & MacKinnon, James G., 1999.
"The Size Distortion Of Bootstrap Tests,"
Cambridge University Press, vol. 15(03), pages 361-376, June.
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