Log-Transform Kernel Density Estimation of Income Distribution

22 Pages Posted: 27 Oct 2014

See all articles by Arthur Charpentier

Arthur Charpentier

Université du Québec à Montréal; Université du Québec à Montréal

Emmanuel Flachaire

Aix-Marseille University

Date Written: September 29, 2014

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. 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

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

Arthur Charpentier (Contact Author)

Université du Québec à Montréal ( email )

PB 8888 Station DownTown
Succursale Centre Ville
Montreal, Quebec H3C3P8
Canada

Université du Québec à Montréal ( email )

Canada

HOME PAGE: http://https://freakonometrics.github.io/

Emmanuel Flachaire

Aix-Marseille University ( email )

3 Avenue Robert Schuman,
France

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