Journal
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
Volume 36, Issue 3, Pages 438-455Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/07350015.2016.1194762
Keywords
Bootstrap; GMM; Grouped data; Income distribution; Over-identifying restriction test
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We propose a unified approach that is flexibly applicable to various types of grouped data for estimating and testing parametric income distributions. To simplify the use of our approach, we also provide a parametric bootstrap method and show its asymptotic validity. We also compare this approach with existing methods for grouped income data, and assess their finite-sample performance by a Monte Carlo simulation. For empirical demonstrations, we apply our approach to recovering China's income/consumption distributions from a sequence of income/consumption share tables and the U.S. income distributions from a combination of income shares and sample quantiles. Supplementary materials for this article are available online.
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