Abul Naga, R, Stapenhurst, C and Yalonetzky, G orcid.org/0000-0003-2438-0223 (2020) Asymptotic Versus Bootstrap Inference for Inequality Indices of the Cumulative Distribution Function. Econometrics, 8 (1). 8. ISSN 2225-1146
Abstract
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | measurement of inequality; ordered response data; multinomial sampling; large sample distributions; Studentized bootstrap tests; monte carlo experiments |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Feb 2020 15:10 |
Last Modified: | 25 Jun 2023 22:10 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/econometrics8010008 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157400 |