Parametric estimation of income distributions using grouped data: an Approximate Bayesian Computation approach

Mathias Silva

No 2310, AMSE Working Papers from Aix-Marseille School of Economics, France

Abstract: Recent empirical analysis of income distributions are often limited by the exclusive availability of data in a grouped format. This data format is made particularly restrictive by a lack of information on the underlying grouping mechanism and sampling variability of the grouped-data statistics it contains. These restrictions often result in the unavailability of an analytical parametric likelihood function exploiting all information available in the grouped data. Building on recent methods for inference on parametric income distributions for this type of data, this paper explores a new Approximate Bayesian Computation (ABC) approach. ABC overcomes the restrictions posed by grouped data for Bayesian inference through a non-parametric approximation of the likelihood function exploiting simulated data from the income distribution model. Empirical applications of the proposed ABC method in both simulated and World Bank's PovCalNet data illustrate the performance and suitability of the method for the typical formats of grouped data on incomes.

Keywords: Grouped data; Bayesian inference; Generalized Lorenz curve; GB2 (search for similar items in EconPapers)
JEL-codes: C11 C18 C63 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2023-04
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Parametric estimation of income distributions using grouped data: an Approximate Bayesian Computation approach (2023)
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