Package: MTS 1.2.1

MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

Authors:Ruey S. Tsay [aut, cre], David Wood [aut], Jon Lachmann [ctb]

MTS_1.2.1.tar.gz
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MTS.pdf |MTS.html
MTS/json (API)
NEWS

# Install 'MTS' in R:
install.packages('MTS', repos = c('https://rtsay1.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • ibmspko - Monthly simple returns of the stocks of International Business Machines
  • qgdp - Quarterly real gross domestic products of United Kingdom, Canada, and the United States
  • tenstocks - Monthly simple returns of ten U.S. stocks

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

6.49 score 6 stars 6 packages 274 scripts 2.1k downloads 35 mentions 87 exports 18 dependencies

Last updated 3 years agofrom:a78f81c43d. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 11 2024
R-4.5-win-x86_64OKOct 11 2024
R-4.5-linux-x86_64OKOct 11 2024
R-4.4-win-x86_64OKOct 11 2024
R-4.4-mac-x86_64OKOct 11 2024
R-4.4-mac-aarch64OKOct 11 2024
R-4.3-win-x86_64OKOct 11 2024
R-4.3-mac-x86_64OKOct 11 2024
R-4.3-mac-aarch64OKOct 11 2024

Exports:apcaarchTestbacktestBEKK11Btfm2BVARccmcomVolCornerdccFitdccPrediffMEccmECMvarECMvar1EWMAvolFEVdecGrangerTesthfactorKronfitKronidKronpredKronspecMarchTestMCHdiagMCholVMlmmqmsqrtmtCopulaMTSdiagMTSplotMtxprodMtxprod1PIwgtPSIwgtrefECMvarrefECMvar1refKronfitrefREGtsrefSCMfitrefsVARMArefVARrefVARMArefVARXrefVMArefVMAeREGtsREGtspredRLSSCCorSCMfitSCMidSCMid2SCMmodsVARMAsVARMACppsVARMApredSWforetfmtfm1tfm2VARVARMAVARMAcovVARMACppVARMAirfVARMApredVARMAsimVARorderVARorderIVARpredVARpsiVARsVARXVARXirfVARXorderVARXpredVechVechMVMAVMACppVMAeVMAorderVMAsVmissVpmiss

Dependencies:cvarfastICAfBasicsfGarchgbutilsgsslatticeMASSMatrixmvtnormrbibutilsRcppRcppEigenRdpackspatialstabledisttimeDatetimeSeries

Readme and manuals

Help Manual

Help pageTopics
Multivariate Time SeriesMTS-package MTS
Asymptotic Principal Component Analysisapca
ARCH test for univariate time seriesarchTest
Backtesting of a scalar ARIMA modelbacktest
BEKK ModelBEKK11
Back-Test of a Transfer Function Model with Two Input VariablesBtfm2
Bayesian Vector AutoregressionBVAR
Cross-Correlation Matricesccm
Common VolatilitycomVol
Compute the Corner table for transfer function model specificationCorner
Dynamic Cross-Correlation Model FittingdccFit
Preliminary Fitting of DCC ModelsdccPre
Difference of multivariate time seriesdiffM
Extended Cross-Correlation MatricesEccm
Error-Correction VAR ModelsECMvar
Error-Correction VAR Model 1ECMvar1
Exponentially Weighted Moving-Average VolatilityEWMAvol
Forecast Error Variance DecompositionFEVdec
Granger Causality TestGrangerTest
Constrained Factor Modelhfactor
Monthly simple returns of the stocks of International Business Machines (IBM) and Coca Cola (KO) and the S&P Composite index (SP)ibmspko
Fitting a VARMA Model via Kronecker IndexKronfit
Kronecker Index IdentificationKronid
Prediction of a fitted VARMA model via Kronfit, using Kronecker indicesKronpred
Kronecler Index SpecificationKronspec
Multivariate ARCH testMarchTest
Multivariate Conditional Heteroscedastic Model CheckingMCHdiag
Multivariate Cholesky Volatility ModelMCholV
Multivariate Linear ModelMlm
Multivariate Ljung-Box Q Statisticsmq
Square Root Matrixmsqrt
Multivariate t-Copula Volatility ModelmtCopula
MTS Internal FunctionsLminv mFilter refVARs refVMAs revmq VARchi VARecm VARfore VARirf VMApred
Multivariate Time Series Diagnostic CheckingMTSdiag
Multivariate Time Series PlotMTSplot
Polynomial Matrix ProductMtxprod
Alternative Polynomial Matrix ProductMtxprod1
Pi Weight MatricesPIwgt
Psi Wights MatricesPSIwgt
Quarterly real gross domestic products of United Kingdom, Canada, and the United Statesqgdp
Refining Error-Correction Model for VAR seriesrefECMvar
Refining ECM for a VAR processrefECMvar1
Refining VARMA Estimation via Kronecker Index ApproachrefKronfit
Refining a Regression Model with Time Series ErrorsrefREGts
Refining Estimation of VARMA Model via SCM ApproachrefSCMfit
Refining a Seasonal VARMA ModelrefsVARMA
Refining a VAR ModelrefVAR
Refining VARMA EstimationrefVARMA
Refining a VARX ModelrefVARX
Refining VMA ModelsrefVMA
Refining VMA Estimation via the Exact Likelihood MethodrefVMAe
Regression Model with Time Series ErrorsREGts
Prediction of a fitted regression model with time series errorsREGtspred
Recursive Least SquaresRLS
Sample Constrained CorrelationsSCCor
Scalar Component Model FittingSCMfit
Scalar Component IdentificationSCMid
Scalar Component Model Specification IISCMid2
Scalar Component Model specificationSCMmod
Seasonal VARMA Model EstimationsVARMA
Seasonal VARMA Model Estimation (Cpp)sVARMACpp
Prediction of a fitted multiplicative seasonal VARMA modelsVARMApred
Stock-Watson Diffusion Index ForecastsSWfore
Monthly simple returns of ten U.S. stockstenstocks
Transfer Function Modeltfm
Transfer Function Model with One Inputtfm1
Transfer Function Model with Two Input Variablestfm2
Vector Autoregressive ModelVAR
Vector Autoregressive Moving-Average ModelsVARMA
Autocovariance Matrices of a VARMA ModelVARMAcov
Vector Autoregressive Moving-Average Models (Cpp)VARMACpp
Impulse Response Functions of a VARMA ModelVARMAirf
VARMA PredictionVARMApred
Generating a VARMA ProcessVARMAsim
VAR Order SpecificationVARorder
VAR order specification IVARorderI
VAR PredictionVARpred
VAR Psi-weightsVARpsi
VAR Model with Selected LagsVARs
VAR Model with Exogenous VariablesVARX
Impluse response function of a fitted VARX modelVARXirf
VARX Order SpecificationVARXorder
VARX Model PredictionVARXpred
Half-Stacking Vector of a Symmetric MatrixVech
Matrix constructed from output of the Vech Command. In other words, restore the original symmetric matrix from its half-stacking vector.VechM
Vector Moving Average ModelVMA
Vector Moving Average Model (Cpp)VMACpp
VMA Estimation with Exact likelihoodVMAe
VMA Order SpecificationVMAorder
VMA Model with Selected LagsVMAs
VARMA Model with Missing ValueVmiss
Partial Missing Value of a VARMA SeriesVpmiss