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 Errorcorrection VAR models for cointegrated 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 movingaverage volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copulabased volatility models, and lowdimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rankbased 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 crosscovariance matrices of a given VARMA model.
Package details 


Author  Ruey S. Tsay and David Wood 
Maintainer  Ruey S. Tsay <ruey.tsay@chicagobooth.edu> 
License  Artistic License 2.0 
Version  1.0 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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