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Gini estimation under infinite variance

Andrea Fontanari, Nassim Nicholas Taleb and Pasquale Cirillo

Physica A: Statistical Mechanics and its Applications, 2018, vol. 502, issue C, 256-269

Abstract: We study the problems related to the estimation of the Gini index in presence of a fat-tailed data generating process, i.e. one in the stable distribution class with finite mean but infinite variance (i.e. with tail index α∈(1,2)). We show that, in such a case, the Gini coefficient cannot be reliably estimated using conventional nonparametric methods, because of a downward bias that emerges under fat tails. This has important implications for the ongoing discussion about economic inequality.

Keywords: Gini index; Inequality measure; Size distribution; Extremes; α-stable distribution (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:256-269