R: Likelihood ratio test for threshold nonlinearity
tlrt
R Documentation
Likelihood ratio test for threshold nonlinearity
Description
Carry out the likelihood ratio test for threshold nonlinearity, with
the null hypothesis being a normal AR process and the alternative
hypothesis being a TAR model with homogeneous, normally distributed errors.
Usage
tlrt(y, p, d = 1, transform = "no", a = 0.25, b = 0.75,...)
Arguments
y
time series
p
working AR order
d
delay
transform
available transformations: "no" (i.e. use raw data), "log", "log10" and
"sqrt"
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
b
upper percent
...
other arguments to be passed to the ar function which determines the
Ar order, if p is missing
Details
The search for the threshold parameter may be narrower than that defined by the
user as the function attempts to ensure adequate sample size in each
regime of the TAR model.
The p-value of the test is based on large-sample approximation and also
is more reliable for small p-values.
Value
p.value
p-value of the test
test.statistic
likelihood ratio test statistic
a
the actual lower fraction that defines the interval of search
for the threshold; it may differ from the a specified by the user
b
the actual upper fraction that defines the interval of search
for the threshold
Author(s)
Kung-Sik Chan
References
Chan, K.S. (1990). Percentage points of likelihood ratio tests for
threshold autoregression. Journal of Royal Statistical Society, B 53, 3, 691-696.
See Also
Keenan.test, Tsay.test
Examples
data(spots)
pvaluem=NULL
for (d in 1:5){
res=tlrt(sqrt(spots),p=5,d=d,a=0.25,b=0.75)
pvaluem= cbind( pvaluem, round(c(d,signif(c(res$test.statistic,
res$p.value))),3))
}
rownames(pvaluem)=c('d','test statistic','p-value')
pvaluem