Qstat.sb: Stationary Bootstrap for Q statistics

In quantilogram: Cross-Quantilogram

Stationary Bootstrap for Q statistics

Description

Stationary Bootstrap procedure to generate critical values for both Box-Pierece and Ljung-Box type Q-statistics

Usage

Qstat.sb(DATA, vecA, Psize, gamma, Bsize, sigLev)

Arguments

DATA	The original data
vecA	A pair of two probabity values at which sample quantiles are estimated
Psize	The maximum number of lags
gamma	A parameter for the stationary bootstrap
Bsize	The number of repetition of bootstrap
sigLev	The statistical significance level

Details

This function returns critical values for for both Box-Pierece and Ljung-Box type Q-statistics through the statioanry bootstrap proposed by Politis and Romano (1994).

Value

The bootstrap 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. (1994). "The stationary bootstrap." Journal of the American Statistical Association 89.428, pp.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

## Q statistics with lags from 1 to5
Qstat.sb(D, vecA, 5, gamma, Bsize, sigLev)

quantilogram documentation built on March 18, 2022, 5:29 p.m.