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

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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.

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  • Mathias Silva, 2023. "Parametric estimation of income distributions using grouped data: an Approximate Bayesian Computation approach," AMSE Working Papers 2310, Aix-Marseille School of Economics, France.
    • Handle: RePEc:aim:wpaimx:2310
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    Cited by:

    1. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," AMSE Working Papers 2311, Aix-Marseille School of Economics, France.

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    More about this item

    Keywords

    Grouped data; Bayesian inference; Generalized Lorenz curve; GB2;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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