Title: Measures of dispersion and serial dependence in categorical time series
Authors: Weiß, Christian H. 
Language: eng
Keywords: Universitätsbibliographie;Evaluation 2019
Issue Date: 2019
Publisher: MDPI
Document Type: Article
Source: Enthalten in: Econometrics. - Basel : MDPI, 2013. - Online-Ressource. - Bd. 7.2019, 2/17 (Juni), Seite 1-23
Journal / Series / Working Paper (HSU): Econometrics : open access journal
Volume: 7
Issue: 2/17 (Juni)
Page Start: 1
Page End: 23
Publisher Place: Basel
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 serial dependence in categorical time series, we consider three types of -measures. By analyzing bias properties, it is shown that always one of the -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.
Organization Units (connected with the publication): Quantitative Methoden der Wirtschaftswissenschaften 
URL: https://www.mdpi.com/2225-1146/7/2/17/pdf
Publisher DOI: 10.3390/econometrics7020017
Appears in Collections:2019

Show full item record

CORE Recommender

SCOPUSTM   
Citations

6
checked on Dec 2, 2022

Google ScholarTM

Check

Altmetric

Altmetric


Items in openHSU are protected by copyright, with all rights reserved, unless otherwise indicated.