Multifrequency-Band Tests for White Noise Under Heteroscedasticity

Bibliographic Data

ID19418299
AuthorsMengya Liu (0000-0002-8875-6907, Jilin University), Fukang Zhu (0000-0002-8808-8179, Jilin University), Ke Zhu (0000-0002-3504-1262, University of Hong Kong, corresponding author)
Year2022
Volume40
Issue2
Pages799-814
Publication date2022-04-03
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2020.1870478
OpenAlexW3119610980
LanguageEN
References cited35

This article proposes a new family of multifrequency-band tests for the white noise hypothesis by using the maximum overlap discrete wavelet packet transform. At each scale, the proposed multifrequency-band test has the chi-square asymptotic null distribution under mild conditions, which allow the data to be heteroscedastic. Moreover, an automatic multifrequency-band test is further proposed by using a data-driven method to select the scale, and its asymptotic null distribution is chi-square with one degree of freedom. Both multifrequency-band and automatic multifrequency-band tests are shown to have the desirable size and power performance by simulation studies, and their usefulness is further illustrated by two applications. As an extension, similar tests are given to check the adequacy of linear time series regression models, based on the unobserved model residuals

Critical band · Data mining · F-test · Heteroscedasticity · Null distribution · Physics · Speech recognition · Statistical hypothesis testing · Statistics · Test statistic · Wavelet · White noise · Complex Systems and Time Series Analysis · Computer Science · Financial Risk and Volatility Modeling · Image and Signal Denoising Methods · Mathematics · Artificial Intelligence

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