Log-Transform Kernel Density Estimation of Income Distribution

Arthur Charpentier and Emmanuel Flachaire

Working Papers from HAL

Abstract: Standard kernel density estimation methods are very often used in practice to estimate density function. 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.

Keywords: nonparametric density estimation; heavy-tail; income distribution; data transformation; lognormal kernel (search for similar items in EconPapers)
Date: 2014-11
New Economics Papers: this item is included in nep-ecm
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01115988
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Related works:
Journal Article: 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 (2015)
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