VARMAsim function

Generating a VARMA Process

Performs simulation of a given VARMA model

VARMAsim(nobs, arlags = NULL, malags = NULL,
  cnst = NULL, phi = NULL, theta = NULL,
  skip = 200, sigma)

Arguments

• nobs: Sample size
• arlags: The exact lags of the VAR matrix polynomial.
• malags: The exact lags of the VMA matrix polynomial.
• cnst: Constant vector, Phi0
• phi: Matrix of VAR coefficient matrices in the order of the given arlags.
• theta: Matrix of VMA coefficient matrices in the order of the given malags.
• skip: The number of initial data to be omitted. Default is 200.
• sigma: Covariance matrix (k-by-k, positive definite) of the innovations

Details

Use multivariate Gaussian distribution to generate random shocks. Then, generate a given VARMA model. The first skip data points were discarded.

Returns

• series: Generated series

• noises: The noise series

References

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

Author(s)

Ruey S. Tsay

Examples

p1=matrix(c(0.2,-0.6,0.3,1.1),2,2)
sig=matrix(c(4,0.8,0.8,1),2,2)
th1=matrix(c(-0.5,0,0,-0.6),2,2)
m1=VARMAsim(300,arlags=c(1),malags=c(1),phi=p1,theta=th1,sigma=sig)
zt=m1$series