Gini autocovariance function used for time series with heavy-tail distributions

DOI10.1002/WICS.1428zbMATH Open1544.62018MaRDI QIDQ6602195

Marcel D. Carcea

Publication date: 11 September 2024

Published in: Wiley Interdisciplinary Reviews. WIREs Computational Statistics (Search for Journal in Brave)

zbMATH Keywords

heavy-tailed time seriesautoregression time series modelsGini autocorrelationGini autocovariancemoving average time series models

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Cites Work

• A note on self-weighted quantile estimation for infinite variance quantile autoregression models
• Limit theory for moving averages of random variables with regularly varying tail probabilities
• More limit theory for the sample correlation function of moving averages
• Limit theory for the sample covariance and correlation functions of moving averages
• Time series: theory and methods.
• Heavy tail modeling and teletraffic data. (With discussions and rejoinder)
• A Gini-based unit root test
• The Gini methodology. A primer on a statistical methodology.
• Gini's mean difference: a superior measure of variability for non-normal distributions
• A Gini-based time series analysis and test for reversibility
• Least absolute deviations estimation for ARCH and GARCH models
• Weighted quantile regression for AR model with infinite variance errors
• A Gini Autocovariance Function for Time Series Modelling
• Assessing Time-Reversibility Under Minimal Assumptions
• Pareto processes
• A Measure Of Association Based On Gin's Mean Difference

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