# tar.sim: Simulate a two-regime TAR model In TSA: Time Series Analysis

## Description

Simulate a two-regime TAR model.

## Usage

 ```1 2``` ```tar.sim(object, ntransient = 500, n = 500, Phi1, Phi2, thd, d, p, sigma1, sigma2, xstart = rep(0, max(p,d)), e) ```

## Arguments

 `object` a TAR model fitted by the tar function; if it is supplied, the model parameters and initial values are extracted from it `ntransient` the burn-in size `n` sample size of the simulated series `Phi1` the coefficient vector of the lower-regime model `Phi2` the coefficient vector of the upper-regime model `thd` threshold `d` delay `p` maximum autoregressive order `sigma1` noise std. dev. in the lower regime `sigma2` noise std. dev. in the upper regime `xstart` initial values for the simulation `e` standardized noise series of size equal to length(xstart)+ntransient+n; if missing, it will be generated as some normally distributed errors

## Details

The two-regime Threshold Autoregressive (TAR) model is given by the following formula:

Y_t = φ_{1,0}+φ_{1,1} Y_{t-1} +…+ φ_{1,p} Y_{t-p_1} +σ_1 e_t, \mbox{ if } Y_{t-d}≤ r

Y_t = φ_{2,0}+φ_{2,1} Y_{t-1} +…+φ_{2,p_2} Y_{t-p}+σ_2 e_t, \mbox{ if } Y_{t-d} > r.

where r is the threshold and d the delay.

## Value

A list containing the following components:

 `y` simulated TAR series `e` the standardized errors

...

Kung-Sik Chan

## References

Tong, H. (1990) "Non-linear Time Series, a Dynamical System Approach," Clarendon Press Oxford "Time Series Analysis, with Applications in R" by J.D. Cryer and K.S. Chan

`tar`

## Examples

 ```1 2 3 4 5``` ```set.seed(1234579) y=tar.sim(n=100,Phi1=c(0,0.5), Phi2=c(0,-1.8),p=1,d=1,sigma1=1,thd=-1, sigma2=2)\$y plot(y=y,x=1:100,type='b',xlab="t",ylab=expression(Y[t])) ```

### Example output

```Attaching package: 'TSA'

The following objects are masked from 'package:stats':

acf, arima

The following object is masked from 'package:utils':

tar
```

TSA documentation built on July 2, 2018, 1:04 a.m.