| TARMAur.test.B {tseriesTARMA} | R Documentation |
Unit root supLM test for an integrated MA versus a stationary TARMA process
Description
Implements a supremum Lagrange Multiplier unit root test for the null hypothesis of a integrated MA process versus a stationary TARMA process.
Usage
TARMAur.test.B(
x,
B = 1000,
pa = 0.25,
pb = 0.75,
thd.range,
method = "ML",
btype = c("wb.r", "wb.n", "iid"),
...
)
Arguments
x |
A univariate vector or time series. |
B |
Integer. Number of bootstrap resamples. Defaults to 1000. |
pa |
Real number in |
pb |
Real number in |
thd.range |
Vector of optional user defined threshold range. If missing then |
method |
Fitting method to be passed to |
btype |
Bootstrap type, can be one of |
... |
Additional arguments to be passed to |
Details
Implements the bootstrap version of TARMAur.test the supremum Lagrange Multiplier test
to test an integrate MA(1) specification versus a stationary TARMA(1,1) specification.
The option btype specifies the type of bootstrap as follows:
wb.rResidual wild bootstrap with Rademacher auxiliary distribution. See (Giannerini et al. 2022).
wb.nResidual wild bootstrap with Normal auxiliary distribution. See (Giannerini et al. 2022).
iidResidual iid bootstrap. See (Goracci et al. 2021).
Value
An object of class TARMAtest with components:
statisticThe value of the supLM statistic.
parameterA named vector:
thresholdis the value that maximises the Lagrange Multiplier values.test.vVector of values of the LM statistic for each threshold given in
thd.range.thd.rangeRange of values of the threshold.
fit.ARMAThe null model: IMA(1) fit over
x.sigma2Estimated innovation variance from the IMA fit.
data.nameA character string giving the name of the data.
p.valueThe bootstrap p-value of the test.
methodA character string indicating the type of test performed.
dThe delay parameter.
paLower threshold quantile.
TbThe bootstrap null distribution.
Author(s)
Simone Giannerini, simone.giannerini@unibo.it
Greta Goracci, greta.goracci@unibz.it
References
-
Chan K, Giannerini S, Goracci G, Tong H (2024). “Testing for threshold regulation in presence of measurement error.” Statistica Sinica, 34(3). https://doi.org/10.5705/ss.202022.0125.
See Also
TARMAur.test for the asymptotic version of the test. print.TARMAtest for the print method.
Examples
## a TARMA(1,1,1,1)
set.seed(123)
x1 <- TARMA.sim(n=100, phi1=c(0.5,-0.5), phi2=c(0.0,0.8), theta1=0.5, theta2=0.5, d=1, thd=0.2)
TARMAur.test.B(x1, B=100) # B=100 for speedup
## a IMA(1,1)
x2 <- arima.sim(n=100, model=list(order = c(0,1,1),ma=0.6))
TARMAur.test.B(x2, B=100) # B=100 for speedup