Last data update: 2014.03.03
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R: Estimation of a TAR model
Estimation of a TAR model
Description
Estimation of a two-regime TAR model.
Usage
tar(y, p1, p2, d, is.constant1 = TRUE, is.constant2 = TRUE, transform = "no",
center = FALSE, standard = FALSE, estimate.thd = TRUE, threshold,
method = c("MAIC", "CLS")[1], a = 0.05, b = 0.95, order.select = TRUE, print = FALSE)
Arguments
y |
time series
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p1 |
AR order of the lower regime
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p2 |
AR order of the upper regime
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d |
delay parameter
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is.constant1 |
if True, intercept included in the lower regime, otherwise
the intercept is fixed at zero
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is.constant2 |
similar to is.constant1 but for the upper regime
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transform |
available transformations: "no" (i.e. use raw data), "log", "log10" and
"sqrt"
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center |
if set to be True, data are centered before analysis
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standard |
if set to be True, data are standardized before analysis
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estimate.thd |
if True, threshold parameter is estimated, otherwise
it is fixed at the value supplied by threshold
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threshold |
known threshold value, only needed to be supplied if estimate.thd is set to be False.
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method |
"MAIC": estimate the TAR model by minimizing the AIC;
"CLS": estimate the TAR model by the method of Conditional Least Squares.
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a |
lower percent; the threshold is searched over the interval defined by the
a*100 percentile to the b*100 percentile of the time-series variable
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b |
upper percent
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order.select |
If method is "MAIC", setting order.select to True will
enable the function to further select the AR order in
each regime by minimizing
AIC
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print |
if True, the estimated model will be printed
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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 of class "TAR" which can be further processed by the
by the predict and tsdiag functions.
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
predict.TAR ,
tsdiag.TAR ,
tar.sim ,
tar.skeleton
Examples
data(prey.eq)
prey.tar.1=tar(y=log(prey.eq),p1=4,p2=4,d=3,a=.1,b=.9,print=TRUE)
Results
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