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Classical and Bayesian Inference for Income Distributions using Grouped Data

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  • Tobias Eckernkemper
  • Bastian Gribisch

Abstract

We propose a general framework for Maximum Likelihood (ML) and Bayesian estimation of income distributions based on grouped data information. The asymptotic properties of the ML estimators are derived and Bayesian parameter estimates are obtained by Monte Carlo Markov Chain (MCMC) techniques. A comprehensive simulation experiment shows that obtained estimates of the income distribution are very precise and that the proposed estimation framework improves the statistical precision of parameter estimates relative to the classical multinomial likelihood. The estimation approach is finally applied to a set of countries included in the World Bank database PovcalNet.

Suggested Citation

  • Tobias Eckernkemper & Bastian Gribisch, 2021. "Classical and Bayesian Inference for Income Distributions using Grouped Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(1), pages 32-65, February.
    • Handle: RePEc:bla:obuest:v:83:y:2021:i:1:p:32-65
    DOI: 10.1111/obes.12396
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    References listed on IDEAS

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    1. Griffiths, William & Hajargasht, Gholamreza, 2015. "On GMM estimation of distributions from grouped data," Economics Letters, Elsevier, vol. 126(C), pages 122-126.
    2. Kazuhiko Kakamu & Haruhisa Nishino, 2019. "Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 625-645, August.
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    Cited by:

    1. Mathias Silva, 2023. "Parametric estimation of income distributions using grouped data: an Approximate Bayesian Computation approach [Working Papers / Documents de travail]," Working Papers hal-04066544, HAL.
    2. Kazuhiko Kakamu, 2022. "Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data," Papers 2210.05115, arXiv.org, revised Sep 2023.
    3. Bolch, Kimberly B. & Ceriani, Lidia & López-Calva, Luis F., 2022. "The arithmetics and politics of domestic resource mobilization for poverty eradication," World Development, Elsevier, vol. 149(C).
    4. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," AMSE Working Papers 2311, Aix-Marseille School of Economics, France.
    5. Tsvetana Spasova, 2024. "Estimating Income Distributions From Grouped Data: A Minimum Quantile Distance Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2079-2096, October.
    6. Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print hal-04347292, HAL.

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