The paper aims at proposing a small area estimation strategy for the Theil Index, an entropy-based inequality measure. Specifically, we have developed an area-level model of its relative index, i.e. Theil index over its maximum, which has more manageable support between 0 and 1. Classical proposals in area-level context for measures defined on the unit interval are mostly based on proportions modelling and show limitations when dealing with asymmetric heavy-tailed data, such as in our case. We propose a Hierarchical Bayes model with alternative likelihood assumptions based on a particular Beta mixture, providing a more flexible framework.
De Nicolò, S., Pacei, S. (2022). Small Area Estimation of Relative Inequality Indices using Mixtures of Beta. Pearson.
Small Area Estimation of Relative Inequality Indices using Mixtures of Beta
De Nicolò, S.;Pacei, S.
2022
Abstract
The paper aims at proposing a small area estimation strategy for the Theil Index, an entropy-based inequality measure. Specifically, we have developed an area-level model of its relative index, i.e. Theil index over its maximum, which has more manageable support between 0 and 1. Classical proposals in area-level context for measures defined on the unit interval are mostly based on proportions modelling and show limitations when dealing with asymmetric heavy-tailed data, such as in our case. We propose a Hierarchical Bayes model with alternative likelihood assumptions based on a particular Beta mixture, providing a more flexible framework.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
