Asymptotic Versus Bootstrap Inference for Inequality Indices of the Cumulative Distribution Function
Ramses Abul Naga,
Christopher Stapenhurst () and
Gaston Yalonetzky ()
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Christopher Stapenhurst: Department of Economics, University of Edinburgh, Edinburgh EH8 9AB, UK
Econometrics, 2020, vol. 8, issue 1, 1-15
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.
Keywords: measurement of inequality; ordered response data; multinomial sampling; large sample distributions; Studentized bootstrap tests; monte carlo experiments (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:8:y:2020:i:1:p:8-:d:325486
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