Chapter 7. Bidimensional Asymptotic Normality of the Moving Kernel Poverty Index Estimate

Abstract

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.