Abstract
We present a novel approach to test for heteroscedasticity of a nonstationary time series that is based on Gini’s mean difference of logarithmic local sample variances. In order to analyse the large sample behaviour of our test statistic, we establish new limit theorems for U-statistics of dependent triangular arrays. We derive the asymptotic distribution of the test statistic under the null hypothesis of a constant variance and show that the test is consistent against a large class of alternatives, including multiple structural breaks in the variance. Our test is applicable even in the case of nonstationary processes, assuming a locally stationary mean function. The performance of the test and its comparatively low computation time are illustrated in an extensive simulation study. As an application, we analyse Google Trends data, monitoring the relative search interest for the topic “global warming.”
Funding Statement
The research was supported by the DFG Collaborative Research Center 823 “Statistical Modelling of Nonlinear Dynamic Processes.” The first author was additionally supported by the Friedrich-Ebert-Stiftung.
Acknowledgements
The authors would like to thank the Associate Editor and the two anonymous referees for their careful reading of the manuscript and their insightful comments which helped to improve the presentation of the paper significantly.
Funding Statement
The research was supported by the DFG Collaborative Research Center 823 “Statistical Modelling of Nonlinear Dynamic Processes.” The first author was additionally supported by the Friedrich-Ebert-Stiftung.
Acknowledgements
The authors would like to thank the Associate Editor and the two anonymous referees for their careful reading of the manuscript and their insightful comments which helped to improve the presentation of the paper significantly.
Citation
Sara K. Schmidt. Max Wornowizki. Roland Fried. Herold Dehling. "An asymptotic test for constancy of the variance under short-range dependence." Ann. Statist. 49 (6) 3460 - 3481, December 2021. https://doi.org/10.1214/21-AOS2092
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