TY - JOUR
T1 - Asymptotic Versus Bootstrap Inference for Inequality Indices of the Cumulative Distribution Function
AU - Abul Naga, Ramses
AU - Stapenhurst, Christopher
AU - Yalonetzky, Gaston
N1 - Funding: This research received no external funding.
Acknowledgments: We are grateful to three anonymous referees, Karim Abadir, Martin Biewen and Emmanuel Flachaire for helpful comments and suggestions. The paper was written in part while Abul Naga was a visitor at Aix-Marseille University. The author wishes to thank the Iméra Institute of Advanced Studies and the Aix-Marseille School of Economics for their hospitality.
Author Contributions: Conceptualization, R.A.N., C.S. and G.Y.; methodology, R.A.N., C.S. and G.Y.; software, R.A.N., C.S. and G.Y.; formal analysis, R.A.N., C.S. and G.Y.; writing–original draft preparation, R.A.N., C.S. and G.Y.; writing–review and editing, R.A.N., C.S. and G.Y.; visualization, R.A.N., C.S. and G.Y. All authors have read and agreed to the published version of the manuscript.
PY - 2020/2/26
Y1 - 2020/2/26
N2 - 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.
AB - 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.
KW - measurement of inequality
KW - ordered response data
KW - multinomial sampling
KW - large sample distributions
KW - ; Studentized bootstrap tests
KW - Monte Carlo experiments
KW - Large sample distributions
KW - Measurement of inequality
KW - Ordered response data
KW - Studentized bootstrap tests
KW - Monte carlo experiments
KW - Multinomial sampling
UR - http://www.scopus.com/inward/record.url?scp=85084836571&partnerID=8YFLogxK
U2 - 10.3390/econometrics8010008
DO - 10.3390/econometrics8010008
M3 - Article
VL - 8
JO - Econometrics
JF - Econometrics
SN - 2225-1146
IS - 1
M1 - 8
ER -
