Author(s)

C-René Dominique

Department of Economics, Laval University, Quebec City, Canada.

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.

Income Inequality, Multifractals, Strange Attractors, Singularity Spectrum, Correlation Dimension

Dominique, C. (2018) Could Noise Spectra of Strange Attractors Better Explain Wealth and Income Inequalities? Evidence from the S&P-500 Index. Modern Economy, 9, 449-462. doi: 10.4236/me.2018.93030.