Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247556 
Year of Publication: 
2020
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 8 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-15
Publisher: 
MDPI, Basel
Abstract: 
We examine the performance of asymptotic inference as well as bootstrap tests for the Alphabeta and Kobus-Mi±o´s 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.
Subjects: 
large sample distributions
measurement of inequality
monte carlo experiments
multinomial sampling
ordered response data
Studentized bootstrap tests
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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