TITLE:
Could Noise Spectra of Strange Attractors Better Explain Wealth and Income Inequalities? Evidence from the S&P-500 Index
AUTHORS:
C-René Dominique
KEYWORDS:
Income Inequality, Multifractals, Strange Attractors, Singularity Spectrum, Correlation Dimension
JOURNAL NAME:
Modern Economy,
Vol.9 No.3,
March
22,
2018
ABSTRACT: 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.