On the Super-Additivity and Estimation Biases of Quantile ContributionsReport as inadecuate

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1 NYU Polytechnic School of Engineering 2 CES - Centre d-économie de la Sorbonne 3 Riskdata

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

Author: Nassim Nicholas Taleb - Raphaël Douady -

Source: https://hal.archives-ouvertes.fr/


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