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Open AccessArticle

Top Incomes, Heavy Tails, and Rank-Size Regressions

1
Aix-Marseille School of Economics, 5 Boulevard Maurice Bourdet CS 50498, 13205 Marseille CEDEX 01, France
2
Department of Economics, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Econometrics 2018, 6(1), 10; https://doi.org/10.3390/econometrics6010010
Received: 19 November 2017 / Revised: 18 February 2018 / Accepted: 20 February 2018 / Published: 2 March 2018
(This article belongs to the Special Issue Econometrics and Income Inequality)
In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading parametric income models (so the upper income tail is less heavy than estimated), which leads to test size distortions and undermines inference. For practical work, we propose a sensitivity analysis based on regression diagnostics in order to assess the likely impact of the distortion. The methods are illustrated using data on top incomes in the UK. View Full-Text
Keywords: top incomes; heavy tails; rank size regression; extreme value index; regular variation top incomes; heavy tails; rank size regression; extreme value index; regular variation
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Schluter, C. Top Incomes, Heavy Tails, and Rank-Size Regressions. Econometrics 2018, 6, 10.

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