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Econometrics and Income Inequality
Open AccessArticle

Asymptotic Versus Bootstrap Inference for Inequality Indices of the Cumulative Distribution Function

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Business School, University of Aberdeen, Old Aberdeen AB24 3UE, UK
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Department of Economics, University of Edinburgh, Edinburgh EH8 9AB, UK
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Business School, University of Leeds, Leeds LS2 9JT, UK
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Author to whom correspondence should be addressed.
Econometrics 2020, 8(1), 8; https://doi.org/10.3390/econometrics8010008
Received: 12 December 2017 / Accepted: 18 February 2020 / Published: 26 February 2020
(This article belongs to the Special Issue Econometrics and Income Inequality)
We examine the performance of asymptotic inference as well as bootstrap tests for the Alphabeta and Kobus–Miłoś family of inequality indices for ordered response data. We use Monte Carlo experiments to compare the empirical size and statistical power of asymptotic inference and the Studentized bootstrap test. In a broad variety of settings, both tests are found to have similar rejection probabilities of true null hypotheses, and similar power. Nonetheless, the asymptotic test remains correctly sized in the presence of certain types of severe class imbalances exhibiting very low or very high levels of inequality, whereas the bootstrap test becomes somewhat oversized in these extreme settings. View Full-Text
Keywords: measurement of inequality; ordered response data; multinomial sampling; large sample distributions; Studentized bootstrap tests; monte carlo experiments measurement of inequality; ordered response data; multinomial sampling; large sample distributions; Studentized bootstrap tests; monte carlo experiments
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Abul Naga, R.; Stapenhurst, C.; Yalonetzky, G. Asymptotic Versus Bootstrap Inference for Inequality Indices of the Cumulative Distribution Function. Econometrics 2020, 8, 8.

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