HDTSA: High Dimensional Time Series Analysis Tools
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.
package
Useful links:
Chen lin, Jinyuan Chang, Qiwei Yao Maintainer: Chen linlinchen@smail.swufe.edu.cn