Top Incomes, Heavy Tails, and Rank-Size Regressions

Christian Schluter

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Abstract: 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)
Date: 2018-03
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-01978497
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Citations: View citations in EconPapers (5)

Published in Econometrics, 2018, 6 (1), pp.10. ⟨10.3390/econometrics6010010⟩

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Journal Article: Top Incomes, Heavy Tails, and Rank-Size Regressions (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01978497

DOI: 10.3390/econometrics6010010

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