Information theoretic approaches to income density estimation with an application to the U.S. income data
Sung Y. Park () and
Anil K. Bera ()
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Anil K. Bera: University of Illinois
The Journal of Economic Inequality, 2018, vol. 16, issue 4, No 1, 486 pages
Abstract The size distribution of income is the basis of income inequality measures which in turn are needed for evaluation of social welfare. Therefore, proper specification of the income density function is of special importance. In this paper, using information theoretic approach, first, we provide a maximum entropy (ME) characterization of some well-known income distributions. Then, we suggest a class of flexible parametric densities which satisfy certain economic constraints and stylized facts of personal income data such as the weak Pareto law and a decline of the income-share elasticities. Our empirical results using the U.S. family income data show that the ME principle provides economically meaningful and a very parsimonious and, at the same time, flexible specification of the income density function.
Keywords: Income density estimation; Information theoretic approach; Maximum entropy; Weak Pareto law (search for similar items in EconPapers)
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