A Novel Correction for the Adjusted Box-Pierce Test

Authors

Sidy Danioko, Chapman UniversityFollow
Jianwei Zheng, Chapman UniversityFollow
Kyle Anderson, Chapman University
Alexander Barrett, Chapman UniversityFollow
Cyril S. Rakovski, Chapman UniversityFollow

Document Type

Article

Publication Date

5-19-2022

Abstract

The classical Box-Pierce and Ljung-Box tests for auto-correlation of residuals possess severe deviations from nominal type I error rates. Previous studies have attempted to address this issue by either revising existing tests or designing new techniques. The Adjusted Box-Pierce achieves the best results with respect to attaining type I error rates closer to nominal values. This research paper proposes a further correction to the adjusted Box-Pierce test that possesses near perfect type I error rates. The approach is based on an inflation of the rejection region for all sample sizes and lags calculated via a linear model applied to simulated data that encompasses a large range of data scenarios. Our results show that the new approach possesses the best type I error rates of all goodness-of-fit time series statistics.

Comments

This article was originally published in Frontiers in Applied Mathematics and Statistics, volume 8, in 2022. https://doi.org/10.3389/fams.2022.873746

Recommended Citation

Danioko S, Zheng J, Anderson K, Barrett A and Rakovski CS (2022) A Novel Correction for the Adjusted Box-Pierce Test. Front. Appl. Math. Stat. 8:873746. https://doi.org/10.3389/fams.2022.873746

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.