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HDTSA (version 1.0.4)

High Dimensional Time Series Analysis Tools

Description

Procedures for high-dimensional time series analysis including factor analysis proposed by Lam and Yao (2012) and Chang, Guo and Yao (2015) ,martingale difference test proposed by Chang, Jiang and Shao (2022) in press,principal component analysis proposed by Chang, Guo and Yao (2018) , identifying cointegration 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) .

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Install

install.packages('HDTSA')

Monthly Downloads

820

Version

1.0.4

License

GPL-3

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Maintainer

Chen Lin

Last Published

September 3rd, 2024

Functions in HDTSA (1.0.4)

SpecMulTest

Statistical inference for high-dimensional spectral density matrix
UR_test

Testing for unit roots based on sample autocovariances
Factors

Factor modeling: Inference for the number of factors
HDSReg

High dimensional stochastic regression with latent factors
WN_test

Testing for white noise hypothesis in high dimension
MartG_test

Testing for martingale difference hypothesis in high dimension
SpecTest

Statistical inference for high-dimensional spectral density matrix
DGP.CP

Data generate process of matrix CP-factor model
PCA_TS

Principal component analysis for time serise
CP_MTS

Estimation of matrix CP-factor model
Coint

Identifying cointegration rank of given time series