Artikel
Asymptotic versus bootstrap inference for inequality indices of the cumulative distribution function
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.
- Sprache
-
Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 8 ; Year: 2020 ; Issue: 1 ; Pages: 1-15 ; Basel: MDPI
- Klassifikation
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Wirtschaft
- Thema
-
large sample distributions
measurement of inequality
monte carlo experiments
multinomial sampling
ordered response data
Studentized bootstrap tests
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Abul Naga, Ramses H.
Stapenhurst, Christopher
Yalonetzky, Gastón
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2020
- DOI
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doi:10.3390/econometrics8010008
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Abul Naga, Ramses H.
- Stapenhurst, Christopher
- Yalonetzky, Gastón
- MDPI
Entstanden
- 2020
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