uTAR: Estimation of a Univariate Two-Regime SETAR Model

In ConvFuncTimeSeries/test_t: Nonlinear time series analysis

Description

Estimation of a univariate two-regime SETAR model, including threshold value. The procedure of Li and Tong (2016) is used to search for the threshold.

Usage

uTAR(y, p1, p2, d = 1, thrV = NULL, Trim = c(0.15, 0.85), k0 = 300,
  include.mean = T, thrQ = c(0, 1))

Arguments

y	a vector of time series.
p1, p2	AR-orders of regime 1 and regime 2.
d	delay for threshold variable, default is 1.
thrV	threshold variable. If thrV is not null, it must have the same length as that of y.
Trim	lower and upper quantiles for possible threshold values.
k0	the maximum number of threshold values to be evaluated. If the sample size is large (> 3000), then k0 = floor(nT*0.5).The default is k0=300. But k0 = floor(nT*0.8) if nT < 300.
include.mean	a logical value indicating whether constant terms are included.
thrQ	lower and upper quantiles to search for threshold value.

Value

uTAR returns a list with components:

data	the data matrix, y.
arorder	AR orders of regimes 1 and 2.
delay	the delay for threshold variable.
residuals	estimated innovations.
coef	a 2-by-(p+1) matrices. The first row show the estimation results in regime 1, and the second row shows these in regime 2.
sigma	estimated innovational covariance matrices of regimes 1 and 2.
nobs	numbers of observations in regimes 1 and 2.
model1,model2	estimated models of regimes 1 and 2.
thr	threshold value.
D	a set of threshold values.
RSS	RSS
AIC	AIC value
cnst	logical values indicating whether the constant terms are included in regimes 1 and 2.
sresi	standardized residuals.

References

Li, D., and Tong. H. (2016) Nested sub-sample search algorithm for estimation of threshold models. Statisitca Sinica, 1543-1554.

Examples

arorder=rep(1,2)
ar.coef=matrix(c(0.7,-0.8),2,1)
y=uTAR.sim(100,arorder,ar.coef,1,0)
est=uTAR(y$series,1,1,1,y$series,c(0.2,0.8),100,TRUE,c(0.2,0.8))

ConvFuncTimeSeries/test_t documentation built on May 29, 2019, 1:39 p.m.