Log-Transform Kernel Density Estimation of Income Distribution

Arthur Charpentier (1) , Emmanuel Flachaire (2)

1 UQAM - Université du Québec à Montréal = University of Québec in Montréal
2 GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille

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.

Mots clés: nonparametric density estimation; heavy-tail; income distribution; data transformation; lognormal kernel

Domaines: Economies et finances

Arthur Charpentier, Emmanuel Flachaire. Log-Transform Kernel Density Estimation of Income Distribution. 2014. ⟨halshs-01115988⟩