TAR.simu: Simulated data from TAR model

In BAYSTAR: On Bayesian Analysis of Threshold Autoregressive Models

Simulated data from TAR model

Description

To generate the simulated data from TAR(2;p1,p2) model.

Usage

TAR.simu(nob, p1, p2, ph.1, ph.2, sig.1, sig.2, lagd, thres, lagp1, lagp2)

Arguments

nob	Number of observations that we want to simulate.
p1	Number of AR coefficient in regime one.
p2	Number of AR coefficient in regime two.
ph.1	The vector of AR parameters in regime one.
ph.2	The vector of AR parameters in regime two.
sig.1	The error terms in regime one.
sig.2	The error terms in regime two.
lagd	The delay lag parameter.
thres	The threshold parameter.
lagp1	The vector of non-zero autoregressive lags for the lower regime. (regime one); e.g. An AR model with p1=3, it could be non-zero lags 1,3, and 5 would set lagp1<-c(1,3,5).
lagp2	The vector of non-zero autoregressive lags for the upper regime. (regime two)

Author(s)

Cathy W.S. Chen, Edward Lin

Examples

## Set the true values of all parameters
nob<- 2000                ## No. of observations
lagd<- 1                  ## delay lag of threshold variable
r<- 0.4                   ## r is the threshold value
sig.1<- 0.8; sig.2<- 0.5  ## variances of error distributions for two regimes
p1<- 2; p2<- 2            ## No. of covariate in two regimes
ph.1<- c(0.1,-0.4,0.3)    ## mean coefficients for regime 1
ph.2<- c(0.2,0.3,0.3)     ## mean coefficients for regime 2
lagp1<-1:2
lagp2<-1:2

yt<- TAR.simu(nob,p1,p2,ph.1,ph.2,sig.1,sig.2,lagd,r,lagp1,lagp2)

BAYSTAR documentation built on May 15, 2022, 1:06 a.m.