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Log-Transform Kernel Density Estimation of Income Distribution

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.
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https://hal-amu.archives-ouvertes.fr/hal-01457340
Contributor : Patrice Cacciuttolo <>
Submitted on : Monday, February 6, 2017 - 1:51:51 PM
Last modification on : Wednesday, August 5, 2020 - 3:15:31 AM

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Arthur Charpentier, Emmanuel Flachaire. Log-Transform Kernel Density Estimation of Income Distribution. Actualite Economique, Ecole des Hautes Etudes Commerciales, 2015, 91 (1-2), pp.141--159. ⟨10.7202/1036917ar⟩. ⟨hal-01457340⟩

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