Inference on inequality measures: A Monte Carlo experiment
AbstractTwo broad types of method tend to be used to estimate the sampling distribution of inequality measure estimators: analytical asymptotic approximations and resampling-based procedures (e.g. thebootstrap). The present paper attempts to check the coverage performance, in large samples, of a series of standard estimators of both types so as to provide a yardstick to choose among competing alternatives. Two sampling schemes are considered: simple random sampling and clustered sampling. The comparison is made using a Monte Carlo experiment and an application to Belgian data. It turns out that neither basic bootstrap procedures nor asymptotic approximations significantly outperform its competitors. Both yield acceptable estimates (especially in random samples) provided that sampling design is taken into account. Copyright Springer-Verlag 2002
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Bibliographic InfoArticle provided by Springer in its journal Journal of Economics.
Volume (Year): 9 (2002)
Issue (Month): 1 (December)
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Web page: http://www.springerlink.com/link.asp?id=108909
Inequality Measures; Inference; Clustered Sampling; D31; D63; I32;
Other versions of this item:
- Philippe Kerm, 2002. "Inference on inequality measures: A Monte Carlo experiment," Journal of Economics, Springer, vol. 77(1), pages 283-306, December.
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
- D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
- I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
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