VARMAsim: Generating a VARMA Process

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

Generating a VARMA Process

Description

Performs simulation of a given VARMA model

Usage

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.

Value

series	Generated series
noises	The noise series

Author(s)

Ruey S. Tsay

References

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

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

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