Using a Gini-Based Methodology for Analyzing Time Series

• Activity Number: 235
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Edna Schechtman*+ and Amit Shelef
• Companies: Ben Gurion University of the Negev and Shamoon College of Engineering
• Keywords: Gini Mean Difference ; autocorrelation ; Gini correlation

Abstract

Most of the literature for analyzing time series measure dispersion using the variance. In this research we use an alternative but parallel framework for analyzing time-series: we use the Gini's Mean Difference (GMD) as an alternative index of variability. The Gini methodology is a rank-based methodology, which takes into account both the variate values and the ranks. It relies only on first order moment assumptions hence it is valid for a wider range of distributions. The GMD shares many properties with the variance, but can be more informative about the properties of distributions that depart from normality. We use one advantage of the Gini:.there are two Gini-autocorrelation functions for each pair of variables, which are not necessarily equal. The difference between them, when it exists, can be informative and may assist to identify models with underlying heavy tailed and non-normal innovations. We suggest using Gini-correlograms, a simple graphical tool, to check the symmetry assumption which is natural in the existing methodology.