Log-Transform Kernel Density Estimation of Income Distribution

Arthur Charpentier () and Emmanuel Flachaire
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Arthur Charpentier: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique, UQAM - Université du Québec à Montréal = University of Québec in Montréal

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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: Economie; quantitative (search for similar items in EconPapers)
Date: 2015-03
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Citations: View citations in EconPapers (5)

Published in Actualite Economique, 2015, 91 (1-2), pp.141--159. ⟨10.7202/1036917ar⟩

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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 (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01457340

DOI: 10.7202/1036917ar

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