Abstracts
Abstract
Standard kernel density estimation methods are very often used in practice to estimate density functions. 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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Appendices
Acknowledgements
We are grateful to Karim Abadir and Taoufik Bouezmarni for helpful comments. The first author acknowledges the support of the Natural Sciences and Engineering Research Council of Canada. The second author acknowledges the support of the Institut Universitaire de France, the Aix-Marseille School of Economics and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the Investissements d’Avenir French Government program, managed by the French National Research Agency (ANR).
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