Employing recent results on the asymptotic theory for Zenga’s index, based on the asymptotic expansion of the inequality index, we present confidence intervals in cross-sectional and longitudinal settings. Simulation results are shown to assess the performance, in size and effective coverage, of the inferential procedure. Finally an application is given by evaluating confidence intervals for Zenga’s index for income distributions in 15 EU countries.
Greselin, F., Pasquazzi, L. (2011). Estimating Gini's and Zenga's inequalities on the ECHP dataset. Intervento presentato a: New results and perspectives on inequality and poverty (International Workshop), Milano-Bicocca University (Milan, Italy).
Estimating Gini's and Zenga's inequalities on the ECHP dataset
GRESELIN, FRANCESCA;PASQUAZZI, LEO
2011
Abstract
Employing recent results on the asymptotic theory for Zenga’s index, based on the asymptotic expansion of the inequality index, we present confidence intervals in cross-sectional and longitudinal settings. Simulation results are shown to assess the performance, in size and effective coverage, of the inferential procedure. Finally an application is given by evaluating confidence intervals for Zenga’s index for income distributions in 15 EU countries.
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