Measures of Dispersion and Serial Dependence in Categorical Time Series

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 <inline-formula> <math display="inline"> <semantics> <mi>&#954;</mi> </semantics> </math> </inline-formula>-measures. By analyzing bias properties, it is shown that always one of the <inline-formula> <math display="inline"> <semantics> <mi>&#954;</mi> </semantics> </math> </inline-formula>-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.

Abstract licence: CC BY

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