Open Access
VOL. 1 | 2018 Chapter 7. Bidimensional Asymptotic Normality of the Moving Kernel Poverty Index Estimate
Youssou CISS, Aboubakary DIAKHABY

Editor(s) Hamet SEYDI, Gane Samb LO, Aboubakary DIAKHABY


In this paper we study the kernel estimator for the bidimensional extension of Foster, Greer and Thorbecke class of measures by Duclos et al. (2006a) for the purpose of a dominance approach to multidimensional poverty. The measure they used in their dominance exercise is essentially a generalization, from one to two dimensions, of the FGT index separate poverty aversion parameters for each dimension. The asymptotic normality of the estimator is established. We next indicate how the proposed estimator can generate sequential confidence intervals by a moving kernel process. Our results are extensions of those of Dia (2009) and of Ciss et al. (2016) in one dimension.


Published: 1 January 2018
First available in Project Euclid: 26 September 2019

Digital Object Identifier: 10.16929/sbs/2018.100-02-03

Primary: 62G05

Keywords: asymptotic normality , bi-dimensional extension of Foster , Greer and Thorbecke , moving kernel , poverty aversion , poverty line , Uniform convergence

Rights: Copyright © 2018 The Statistics and Probability African Society

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