FEVdec: Forecast Error Variance Decomposition

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

Forecast Error Variance Decomposition

Description

Computes the forecast error variance decomposition of a VARMA model

Usage

FEVdec(Phi, Theta, Sig, lag = 4)

Arguments

Phi	VAR coefficient matrices in the form Phi=[Phi1, Phi2, ..., Phip], a k-by-kp matrix.
Theta	VMA coefficient matrices in form form Theta=[Theta1, Theta2, ..., Thetaq], a k-by-kq matrix.
Sig	The residual covariance matrix Sigma, a k-by-k positive definite matrix.
lag	The number of lags of forecast errors variance to be computed. Default is 4.

Details

Use the psi-weight matrices to compute the forecast error covariance and use Cholesky decomposition to perform the decomposition

Value

irf	Impulse response matrices
orthirf	Orthogonal impulse response matrices
Omega	Forecast error variance matrices
OmegaR	Forecast error variance decomposition

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 3)

Examples

p1=matrix(c(0.2,-0.6,0.3,1.1),2,2)
theta1=matrix(c(-0.5,0,0,-0.6),2,2)
Sig=matrix(c(3,1,1,1),2,2)
m1=FEVdec(p1,theta1,Sig)
names(m1)

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