Top Incomes, Heavy Tails, and Rank-Size Regressions
Christian Schluter ()
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Christian Schluter: Aix-Marseille School of Economics, 5 Boulevard Maurice Bourdet CS 50498, 13205 Marseille CEDEX 01, France
Econometrics, 2018, vol. 6, issue 1, 1-16
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.
Keywords: top incomes; heavy tails; rank size regression; extreme value index; regular variation (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:6:y:2018:i:1:p:10-:d:134492
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