tar.sim: Simulate a two-regime TAR model

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/tar.sim.R

Description

Simulate a two-regime TAR model.

Usage

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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

...

Author(s)

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

See Also

tar

Examples

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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.