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Measures of Dispersion and Serial Dependence in Categorical Time Series
[journal article]
Abstract The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed se... view more
The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed serial dependence in categorical time series, we consider three types of κ-measures. By analyzing bias properties, it is shown that always one of the κ-measures is related to one of the above-mentioned dispersion measures. For doing statistical inference based on the sample versions of these dispersion and dependence measures, knowledge on their distribution is required. Therefore, we study the asymptotic distributions and bias corrections of the considered dispersion and dependence measures, and we investigate the finite-sample performance of the resulting asymptotic approximations with simulations. The application of the measures is illustrated with real-data examples from politics, economics and biology.... view less
Keywords
time series; variability; statistics; statistical analysis; statistical method; mathematical method; inferential statistics; sample; reliability; distribution; measurement instrument; simulation; ALLBUS
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
Cohen's κ; extropy; nominal variation; signed serial dependence; asymptotic distribution; ALLBUS 1980-2016, ZA4586, Data File (Version 1.0.0)
Document language
English
Publication Year
2019
Page/Pages
p. 1-23
Journal
Econometrics, 7 (2019) 2
DOI
https://doi.org/10.3390/econometrics7020017
ISSN
2225-1146
Status
Published Version; peer reviewed