Autocovariance Matrices of a VARMA Model

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Description

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

Usage

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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 magrices, 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

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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)

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