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A Unified Approach to Estimating and Testing Income Distributions with Grouped Data (published in JOURNAL OF BUSINESS & ECONOMIC STATISTICS)

作者 陳宜廷
摘要 We propose a unified approach which 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.