Inference on inequality measures: A Monte Carlo experiment
Two 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
(This abstract was borrowed from another version of this item.)
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Howes, Stephen & Lanjouw, Jean Olson, 1998. "Does Sample Design Matter for Poverty Rate Comparisons?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
- Christian Schluter & Mark Trede, 2002. "Statistical Inference for Inequality and Poverty Measurement with Dependent Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 493-508, May.
- Cowell, Frank A., 1989. "Sampling variance and decomposable inequality measures," Journal of Econometrics, Elsevier, vol. 42(1), pages 27-41, September.
- Frank Cowell & Christian Schluter, 1998. "Measuring income mobility with dirty data," LSE Research Online Documents on Economics 2079, London School of Economics and Political Science, LSE Library.
- Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
- Mills, Jeffrey A & Zandvakili, Sourushe, 1997.
"Statistical Inference via Bootstrapping for Measures of Inequality,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 12(2), pages 133-50, March-Apr.
- Jeffrey A. Mills & Sourushe Zandvakili, 1999. "Statistical Inference via Bootstrapping for Measures of Inequality," Macroeconomics 9902003, EconWPA.
When requesting a correction, please mention this item's handle: RePEc:kap:jeczfn:v:9:y:2002:i:1:p:283-306. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Rebekah McClure)
If references are entirely missing, you can add them using this form.