Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/233715 
Year of Publication: 
2020
Citation: 
[Journal:] Oxford Bulletin of Economics and Statistics [ISSN:] 1468-0084 [Volume:] 83 [Issue:] 1 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2020 [Pages:] 32-65
Publisher: 
Wiley, Hoboken, NJ
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.
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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