On the Super-Additivity and Estimation Biases of Quantile Contributions

Nassim Nicholas Taleb () and Raphael Douady ()
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Nassim Nicholas Taleb: NYU Polytechnic School of Engineering

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: Sample measures of top centile contributions to the total (concentration) are downward biased, unstable estimators, extremely sensitive to sample size and concave in accounting for large deviations. It makes them particularly unfit in domains with power law tails, especially for low values of the exponent. These estimators can vary over time and increase with the population size, as shown in this article, thus providing the illusion of structural changes in concentration. They are also inconsistent under aggregation and mixing distributions, as the weighted average of concentration measures for A and B will tend to be lower than that from A ∪ B. In addition, it can be shown that under such fat tails, increases in the total sum need to be accompanied by increased sample size of the concentration measurement. We examine the estimation superadditivity and bias under homogeneous and mixed distributions.

Keywords: law of large numbers; fat tails; quantile contribution; Gini coefficient (search for similar items in EconPapers)
Date: 2014-12
Note: View the original document on HAL open archive server: https://hal.science/hal-01149834
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Published in 2014

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Related works:
Journal Article: On the super-additivity and estimation biases of quantile contributions (2015)
Working Paper: On the Super-Additivity and Estimation Biases of Quantile Contributions (2015)
Working Paper: On the Super-Additivity and Estimation Biases of Quantile Contributions (2015)
Working Paper: On the Super-Additivity and Estimation Biases of Quantile Contributions (2014)
Working Paper: On the Super-Additivity and Estimation Biases of Quantile Contributions (2014)
Working Paper: On the Super-Additivity and Estimation Biases of Quantile Contributions (2014)
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