VARMAsim: Generating a VARMA Process

Description Usage Arguments Details Value Author(s) References Examples

Description

Performs simulation of a given VARMA model

Usage

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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

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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

Example output

Warning message:
In matrix(c(0.2, -0.6, 0.3, 1, 1), 2, 2) :
  data length [5] is not a sub-multiple or multiple of the number of rows [2]

MTS documentation built on Oct. 10, 2018, 5:04 p.m.