Artikel
Classical and Bayesian Inference for Income Distributions using Grouped Data
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Oxford Bulletin of Economics and Statistics ; ISSN: 1468-0084 ; Volume: 83 ; Year: 2020 ; Issue: 1 ; Pages: 32-65 ; Hoboken, NJ: Wiley
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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Eckernkemper, Tobias
Gribisch, Bastian
- Event
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Veröffentlichung
- (who)
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Wiley
- (where)
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Hoboken, NJ
- (when)
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2020
- DOI
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doi:10.1111/obes.12396
- Handle
- Last update
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10.03.2025, 11:41 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Eckernkemper, Tobias
- Gribisch, Bastian
- Wiley
Time of origin
- 2020
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