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HDTSA (version 1.0.5)

High Dimensional Time Series Analysis Tools

Description

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 (2023) , 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 (2022) , white noise test proposed by Chang, Yao and Zhou (2017) , CP-decomposition for matrix time series proposed by Chang et al. (2023) and Chang et al. (2024) , and statistical inference for spectral density matrix proposed by Chang et al. (2022) .

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Install

install.packages('HDTSA')

Monthly Downloads

820

Version

1.0.5

License

GPL-3

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Maintainer

Chen Lin

Last Published

December 2nd, 2024

Functions in HDTSA (1.0.5)

predict.mtscp

Make predictions from a "mtscp" object
IPindices

U.S. Industrial Production indices
predict.factors

Make predictions from a "factors" object
predict.tspca

Make predictions from a "tspca" object
WN_test

Testing for white noise hypothesis in high dimension
SpecTest

Global testing for spectral density matrix
PCA_TS

Principal component analysis for vector time series
QWIdata

The national QWI hires data
FamaFrench

Fama-French 10*10 return series
HDSReg

Factor analysis with observed regressors for vector time series
CP_MTS

Estimating the matrix time series CP-factor model
Coint

Identifying the cointegration rank of nonstationary vector time series
DGP.CP

Generating simulated data for the example in Chang et al. (2024)
HDTSA-package

HDTSA: High Dimensional Time Series Analysis Tools
Factors

Factor analysis for vector time series
UR_test

Testing for unit roots based on sample autocovariances
MartG_test

Testing for martingale difference hypothesis in high dimension
SpecMulTest

Multiple testing with FDR control for spectral density matrix