README.md
In Linc2021/HDTSA: High Dimensional Time Series Analysis Tools

An introduction to `HDTSA`

HDTSA

An implementation for high-dimensional time series analysis methods, including: factor model for vector time series 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>, principal component analysis for vector time series proposed by Chang, Guo and Yao (2018) \<doi:10.1214/17-AOS1613>, cointegration analysis 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) \<doi:10.48550/arXiv.2410.05634>, and Statistical inference for high-dimensional spectral density matrix porposed by Chang, Jiang, McElroy and Shao (2023) \<doi:10.48550/arXiv.2212.13686>.

You can install the released version of HDTSA from CRAN with:

install.packages("HDTSA")

Or try the development version on GitHub:

# install.packages("devtools")
devtools::install_github("Linc2021/HDTSA")

This is a basic example which shows you how to solve a unit root test problem :

library(HDTSA)
N=100
Y=arima.sim(list(ar=c(0.9)), n = 2*N, sd=sqrt(1))
con_vec=c(0.45,0.55,0.65)
lagk.vec=c(0,1,2)
UR_test(Y,lagk.vec=lagk.vec, con_vec=con_vec,alpha=0.05)
#> 
#>  Testing for unit roots based on sample autocovariances
#> 
#> Reject the null hypothesis or not with different argument
#>          time_lag=0 time_lag=1 time_lag=2
#> con=0.45          0          0          0
#> con=0.55          0          0          0
#> con=0.65          0          0          0

UR_test(Y,alpha=0.05)
#> 
#>  Testing for unit roots based on sample autocovariances
#> 
#> Reject the null hypothesis or not with different argument
#>          time_lag=0 time_lag=1 time_lag=2 time_lag=3 time_lag=4
#> con=0.55          0          0          0          0          0

Here, we have provided just one example. You can use functions within the package HDTSA to solve other problems. For details, please refer to