MTS: VARMAsim – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

MTS

VARMAsim

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

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

v1.0

Artistic License 2.0

Authors

Ruey S. Tsay and David Wood

Initial release

2018-10-8