In Linc2021/HDTSA: High Dimensional Time Series Analysis Tools

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HDTSA

An implementation for high-dimensional time series analysis methods, including: factor model for vector time series proposed by Lam and Yao (2012) and Chang, Guo and Yao (2015) , martingale difference test proposed by Chang, Jiang and Shao (2022) , principal component analysis for vector time series proposed by Chang, Guo and Yao (2018) , cointegration analysis proposed by Zhang, Robinson and Yao (2019) , unit root test proposed by Chang, Cheng and Yao (2021) , white noise test proposed by Chang, Yao and Zhou (2017) , CP-decomposition for high-dimensional matrix time series proposed by Chang, He, Yang and Yao (2023) and Chang, Du, Huang and Yao (2024) , and Statistical inference for high-dimensional spectral density matrix porposed by Chang, Jiang, McElroy and Shao (2023) .

Installation

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")

Example

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)
UR_test(Y,alpha=0.05)

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

help("HDTSA")

Bug report

Please send an email to Chen Lin(linchen\@smail.swufe.edu.cn{.email}).

Linc2021/HDTSA documentation built on Jan. 29, 2025, 3:08 p.m.