22 Pages Posted: 27 Oct 2014
Date Written: September 29, 2014
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. We first show that a preliminary logarithmic transformation of the data, combined with standard kernel density estimation methods, can provide a much better fit of the overall density estimation. Then, we show that the fit of the bottom of the distribution may not be satisfactory, even if a better fit of the upper tail can be obtained in general.
Keywords: nonparametric density estimation, heavy-tail, income distribution, data transformation, lognormal kernel
JEL Classification: C15
Suggested Citation: Suggested Citation
Charpentier, Arthur and Flachaire, Emmanuel, Log-Transform Kernel Density Estimation of Income Distribution (September 29, 2014). Available at SSRN: https://ssrn.com/abstract=2514882 or http://dx.doi.org/10.2139/ssrn.2514882