mTAR.sim: Generate Two-Regime (VAR) Models

In ConvFuncTimeSeries/test_t: Nonlinear time series analysis

Description

Generates two-regime multivariate vector auto-regressive models.

Usage

mTAR.sim(nob, thr, phi1, phi2, sigma1, sigma2 = NULL, c1 = NULL,
  c2 = NULL, delay = c(1, 1), ini = 500)

Arguments

nob	number of observations.
thr	threshold value.
phi1	VAR coefficient matrix of regime 1.
phi2	VAR coefficient matrix of regime 2.
sigma1	innovational covariance matrix of regime 1.
sigma2	innovational covariance matrix of regime 2.
c1	constant vector of regime 1.
c2	constatn vector of regime 2.
delay	two elements (i,d) with "i" being the component index and "d" the delay for threshold variable.
ini	burn-in period.

Value

mTAR.sim returns a list with following components:

series	a time series following the two-regime multivariate VAR model.
at	innovation of the time series.
threshold	threshold value.
delay	two elements (i,d) with "i" being the component index and "d" the delay for threshold variable.
n1	number of observations in regime 1.
n2	number of observations in regime 2.

Examples

phi1=matrix(c(0.5,0.7,0.3,0.2),2,2)
phi2=matrix(c(0.4,0.6,0.5,-0.5),2,2)
sigma1=matrix(c(1,0,0,1),2,2)
sigma2=matrix(c(1,0,0,1),2,2)
c1=c(0,0)
c2=c(0,0)
delay=c(1,1)
y=mTAR.sim(100,0,phi1,phi2,sigma1,sigma2,c1,c2,delay,ini=500)

ConvFuncTimeSeries/test_t documentation built on May 29, 2019, 1:39 p.m.