HDTSA-package: HDTSA: High Dimensional Time Series Analysis Tools

In HDTSA: High Dimensional Time Series Analysis Tools

HDTSA: High Dimensional Time Series Analysis Tools

Description

The purpose of HDTSA is to address a range of high-dimensional time series problems, which includes solutions to a series of statistical issues, primarily comprising: Procedures for high-dimensional time series analysis including factor analysis proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015) <doi:10.1016/j.jeconom.2015.03.024>,martingale difference test proposed by Chang, Jiang and Shao (2022) <doi:10.1016/j.jeconom.2022.09.001> in press, principal component analysis proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, identifying cointegration proposed by Zhang, Robinson and Yao (2019) <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2021) <doi:10.1093/biomet/asab034>, white noise test proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066>, CP-decomposition for high-dimensional matrix time series proposed by Chang, He, Yang and Yao (2023) <doi:10.1093/jrsssb/qkac011> and Chang, Du, Huang and Yao (2024+), and Statistical inference for high-dimensional spectral density matrix porposed by Chang, Jiang, McElroy and Shao (2023) <doi:10.48550/arXiv.2212.13686>.

Author(s)

Chen lin, Jinyuan Chang, Qiwei Yao Maintainer: Chen lin<[e-mail]>

See Also

Useful links:

• https://github.com/Linc2021/HDTSA

• Report bugs at https://github.com/Linc2021/HDTSA/issues

HDTSA documentation built on April 3, 2025, 11:07 p.m.