VARMAcov: Autocovariance Matrices of a VARMA Model

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

Autocovariance Matrices of a VARMA Model

Description

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

Usage

VARMAcov(Phi = NULL, Theta = NULL, Sigma = NULL, lag = 12, trun = 120)

Arguments

Phi	A k-by-kp matrix consisting of VAR coefficient matrices, Phi = [Phi1, Phi2, ..., Phip].
Theta	A k-by-kq matrix consisting of VMA coefficient matrices, Theta = [Theta1, Theta2, ..., Thetaq]
Sigma	Covariance matrix of the innovations (k-by-k).
lag	Number of cross-covariance matrices to be computed. Default is 12.
trun	The lags of pis-weights used in calculation. Default is 120.

Details

Use psi-weight matrices to compute approximate autocovariance matrices of a VARMA model.

Value

autocov	Autocovariance matrices
ccm	Auto correlation matrices

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 3). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

Examples

Phi=matrix(c(0.2,-0.6,0.3,1.1),2,2)
Sig=matrix(c(4,1,1,1),2,2)
VARMAcov(Phi=Phi,Sigma=Sig)

MTS documentation built on April 11, 2022, 5:07 p.m.