mTAR | R Documentation |
Estimation of a multivariate two-regime SETAR model, including threshold. The procedure of Li and Tong (2016) is used to search for the threshold.
mTAR(
y,
p1,
p2,
thr = NULL,
thrV = NULL,
delay = c(1, 1),
Trim = c(0.1, 0.9),
k0 = 300,
include.mean = TRUE,
score = "AIC"
)
y |
a ( |
p1 |
AR-order of regime 1. |
p2 |
AR-order of regime 2. |
thr |
threshold variable. Estimation is needed if |
thrV |
vector of threshold variable. If it is not null, thrV must have the same sample size of that of y. |
delay |
two elements (i,d) with "i" being the component and "d" the delay for threshold variable. |
Trim |
lower and upper quantiles for possible threshold value. |
k0 |
the maximum number of threshold values to be evaluated. |
include.mean |
logical values indicating whether constant terms are included. |
score |
the choice of criterion used in selection threshold, namely (AIC, det(RSS)). |
mTAR returns a list with the following components:
data |
the data matrix, y. |
beta |
a ( |
arorder |
AR orders of regimes 1 and 2. |
sigma |
estimated innovational covariance matrices of regimes 1 and 2. |
residuals |
estimated innovations. |
nobs |
numbers of observations in regimes 1 and 2. |
model1, model2 |
estimated models of regimes 1 and 2. |
thr |
threshold value. |
delay |
two elements ( |
thrV |
vector of threshold variable. |
D |
a set of positive threshold values. |
RSS |
residual sum of squares. |
information |
overall information criteria. |
cnst |
logical values indicating whether the constant terms are included in regimes 1 and 2. |
sresi |
standardized residuals. |
Li, D., and Tong. H. (2016) Nested sub-sample search algorithm for estimation of threshold models. Statisitca Sinica, 1543-1554.
phi1=matrix(c(0.5,0.7,0.3,0.2),2,2)
phi2=matrix(c(0.4,0.6,0.5,-0.5),2,2)
sigma1=matrix(c(1,0,0,1),2,2)
sigma2=matrix(c(1,0,0,1),2,2)
c1=c(0,0)
c2=c(0,0)
delay=c(1,1)
Trim=c(0.2,0.8)
include.mean=TRUE
y=mTAR.sim(1000,0,phi1,phi2,sigma1,sigma2,c1,c2,delay,ini=500)
est=mTAR(y$series,1,1,0,y$series,delay,Trim,300,include.mean,"AIC")
est2=mTAR(y$series,1,1,NULL,y$series,delay,Trim,300,include.mean,"AIC")
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