LOG-TRANSFORM KERNEL DENSITY ESTIMATION OF INCOME DISTRIBUTION

Arthur Charpentier and Emmanuel Flachaire

L'Actualité Economique, 2015, vol. 91, issue 1-2, 141-159

Abstract: Standard kernel density estimation methods are very often used in practice to estimate density functions. It works well in numerous cases. However, it is known not to work so well with skewed, multimodal and heavy-tailed distributions. Such features are usual with income distributions, defined over the positive support. In this paper, we show that a preliminary logarithmic transformation of the data, combined with standard kernel density estimation methods, can provide a much better fit of the density estimation.

Date: 2015
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Log-Transform Kernel Density Estimation of Income Distribution (2015)
Working Paper: Log-Transform Kernel Density Estimation of Income Distribution (2015)
Working Paper: Log-Transform Kernel Density Estimation of Income Distribution (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:ris:actuec:0116

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