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Classical and Bayesian Inference for Income Distributions using Grouped Data
Author(s) -
Eckernkemper Tobias,
Gribisch Bastian
Publication year - 2021
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12396
Subject(s) - markov chain monte carlo , estimator , econometrics , bayesian probability , statistics , inference , mathematics , bayes estimator , bayesian inference , statistical inference , multinomial distribution , grouped data , data set , computer science , artificial intelligence
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 .