VARMAcov

A k-by-kp matrix consisting of VAR coefficient matrices, 
Phi = [Phi1, Phi2, ..., Phip].

A k-by-kq matrix consisting of VMA coefficient matrices,
Theta = [Theta1, Theta2, ..., Thetaq]

Theta

Covariance matrix of the innovations (k-by-k).

Sigma

Number of cross-covariance matrices to be computed. 
Default is 12.

The lags of pis-weights used in calculation. Default is 120.

trun

Uses psi-weights to compute the autocovariance matrices of 
a VARMA model

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.

VARMAcov: Autocovariance Matrices of a VARMA Model

Description

Usage

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Value

Details

References

Examples