کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5097344 | 1376583 | 2007 | 26 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Asymptotic and bootstrap inference for inequality and poverty measures
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
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چکیده انگلیسی
A random sample drawn from a population would appear to offer an ideal opportunity to use the bootstrap in order to perform accurate inference, since the observations of the sample are IID. In this paper, Monte Carlo results suggest that bootstrapping a commonly used index of inequality leads to inference that is not accurate even in very large samples, although inference with poverty indices is satisfactory. We find that the major cause is the extreme sensitivity of many inequality indices to the exact nature of the upper tail of the income distribution. This leads us to study two non-standard bootstraps, the m out of n bootstrap, which is valid in some situations where the standard bootstrap fails, and a bootstrap in which the upper tail is modelled parametrically. Monte Carlo results suggest that accurate inference can be achieved with this last method in moderately large samples.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Econometrics - Volume 141, Issue 1, November 2007, Pages 141-166
Journal: Journal of Econometrics - Volume 141, Issue 1, November 2007, Pages 141-166
نویسندگان
Russell Davidson, Emmanuel Flachaire,