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