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.
slide
Inequality; Zenga's index; Confidence interval; Inference for inequality measures
English
New results and perspectives on inequality and poverty (International Workshop)
2011
2011
open
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).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/19097
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