Averaging Income Distributions
AbstractVarious inequality and social welfare measures often depend heavily on the choice of a distribution of income. Picking a distribution that best fits the data involves throwing away information and does not allow for the fact that a wrong choice can be made. Instead, Bayesian model averaging utilizes a weighted average of the results from a number of income distributions, with each weight given by the probability that a distribution is 'correct'. In this study, prior densities are placed on mean income, the mode of income and the Gini coefficient for Australian income units with one parent (1997-8). Then, using grouped sample data on incomes, posterior densities for the mean and mode of income and the Gini coefficient are derived for a variety of income distributions. The model-averaged results from these income distributions are obtained. Copyright Blackwell Publishers Ltd and the Board of Trustees of the Bulletin of Economic Research, 2005.
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Bulletin of Economic Research.
Volume (Year): 57 (2005)
Issue (Month): 4 (October)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0307-3378
Other versions of this item:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
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- Danilov, D.L. & Magnus, J.R., 2001. "On the Harm that Pretesting Does," Discussion Paper 2001-37, Tilburg University, Center for Economic Research.
- Griffiths, William E & Chotikapanich, Duangkamon, 1997. "Bayesian Methodology for Imposing Inequality Constraints on a Linear Expenditure System with Demographic Factors," Australian Economic Papers, Wiley Blackwell, vol. 36(69), pages 321-41, December.
- Duangkamon Chotikapanich & William E. Griffiths, 2006. "Bayesian Assessment of Lorenz and Stochastic Dominance in Income Distributions," Department of Economics - Working Papers Series 960, The University of Melbourne.
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