WN_test {HDTSA} | R Documentation |
Testing for white noise hypothesis in high dimension
Description
WN_test()
implements the test proposed in Chang, Yao and Zhou
(2017) for the following hypothesis testing problem:
H_0:\{{\bf y}_t
\}_{t=1}^n\mathrm{\ is\ white\ noise\ \ versus\ \ }H_1:\{{\bf y}_t
\}_{t=1}^n\mathrm{\ is\ not\ white\ noise.}
Usage
WN_test(
Y,
lag.k = 2,
B = 1000,
kernel.type = c("QS", "Par", "Bart"),
pre = FALSE,
alpha = 0.05,
control.PCA = list()
)
Arguments
Y |
An |
lag.k |
The time lag |
B |
The number of bootstrap replications for generating multivariate normally distributed random vectors when calculating the critical value. The default is 1000. |
kernel.type |
The option for choosing the symmetric kernel used
in the estimation of long-run covariance matrix. Available options include:
|
pre |
Logical. If |
alpha |
The significance level of the test. The default is 0.05. |
control.PCA |
A list of control arguments passed to the function
|
Value
An object of class "hdtstest"
, which contains the following
components:
statistic |
The test statistic of the test. |
p.value |
The p-value of the test. |
lag.k |
The time lag used in function. |
kernel.type |
The kernel used in function. |
References
Chang, J., Guo, B., & Yao, Q. (2018). Principal component analysis for second-order stationary vector time series. The Annals of Statistics, 46, 2094–2124. doi:10.1214/17-AOS1613.
Chang, J., Yao, Q., & Zhou, W. (2017). Testing for high-dimensional white noise using maximum cross-correlations. Biometrika, 104, 111–127. doi:10.1093/biomet/asw066.
See Also
Examples
#Example 1
## Generate xt
n <- 200
p <- 10
Y <- matrix(rnorm(n * p), n, p)
res <- WN_test(Y)
Pvalue <- res$p.value
rej <- res$reject