Inference for Income Distributions Using Grouped Data
Gholamreza Hajargasht, William E Griffiths, Joseph Brice, DS Prasada Rao, Duangkamon Chotikapanich
JOURNAL OF BUSINESS & ECONOMIC STATISTICS | AMER STATISTICAL ASSOC | Published : 2012
We develop a general approach to estimation and inference for income distributions using grouped or aggregate data that are typically available in the form of population shares and class mean incomes, with unknown group bounds. We derive generic moment conditions and an optimal weight matrix that can be used for generalized method-of-moments (GMM) estimation of any parametric income distribution. Our derivation of the weight matrix and its inverse allows us to express the seemingly complex GMM objective function in a relatively simple form that facilitates estimation. We show that our proposed approach, which incorporates information on class means as well as population proportions, is more ..View full abstract
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Awarded by Australian Research Council
The authors are grateful to the editor and referees for valuable suggestions. The project was supported by the Australian Research Council Grant DP1094632.