Asymptotic Versus Bootstrap Inference for Inequality Indices of the Cumulative Distribution Function

Ramses Abul Naga* (Corresponding Author), Christopher Stapenhurst, Gaston Yalonetzky

*Corresponding author for this work

Research output: Contribution to journalArticle

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.
Original languageEnglish
Article number8
Number of pages15
JournalEconometrics
Volume8
Issue number1
DOIs
Publication statusPublished - 26 Feb 2020

Keywords

  • measurement of inequality
  • ordered response data
  • multinomial sampling
  • large sample distributions
  • ; Studentized bootstrap tests
  • Monte Carlo experiments

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