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Could Noise Spectra of Strange Attractors Better Explained Wealth and Income Inequalities? Evidence from the S&P-500 Index.

Dominique, C-Rene (2018): Could Noise Spectra of Strange Attractors Better Explained Wealth and Income Inequalities? Evidence from the S&P-500 Index.

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Abstract

SUMMARY: Inequity in wealth and income distributions is ubiquitous and persistent in markets economies. Economists have long suspected that this might be due to the workings of a power law. But studies in financial economics have focused mainly on tail exponent while attempting to recover the Pareto and Zipf’s laws. The estimation of tail exponents from log-log plots, as in stock market returns, produces biased estimators and has little impact on policy. This paper argues that economic time series are output signals of a multifractal process driven by strange attractors. Consequently, estimating noise spectra thrown-up by strange attractors stands to produce a much richer set of information, including the lower and upper bounds of unequal income distribution.

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