| crossq.sb {quantilogram} | R Documentation |
Stationary Bootstrap for the Cross-Quantilogram
Description
Returns critical values for the cross-quantilogram, based on the stationary bootstrap.
Usage
crossq.sb(DATA, vecA, k, gamma, Bsize, sigLev)
Arguments
DATA |
The original data matrix |
vecA |
A pair of two probability values at which sample quantiles are estimated |
k |
A lag order |
gamma |
A parameter for the stationary bootstrap |
Bsize |
The number of repetition of bootstrap |
sigLev |
The statistical significance level |
Details
This function generates critical values for for the cross-quantilogram, using the stationary bootstrap in Politis and Romano (1994).
Value
The boostrap critical values
Author(s)
Heejoon Han, Oliver Linton, Tatsushi Oka and Yoon-Jae Whang
References
Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series." Journal of Econometrics, 193(1), 251-270.
Politis, Dimitris N., and Joseph P. Romano. "The stationary bootstrap." Journal of the American Statistical Association 89.428 (1994): 1303-1313.
Examples
data("sys.risk") ## data source
D = sys.risk[,c("Market", "JPM")] ## data: 2 variables
# probability levels for the 2 variables
vecA = c(0.1, 0.5)
## setup for stationary bootstrap
gamma = 1/10 ## bootstrap parameter depending on data
Bsize = 5 ## small size, 5, for test
sigLev = 0.05 ## significance level
## cross-quantilogram with the lag of 5
crossq.sb(D, vecA, 5, gamma, Bsize, sigLev)