quantilogram (version 2.0.1)

Qstat.reg.sb: 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.reg.sb(DATA1, DATA2, vecA, Psize, gamma, Bsize, sigLev)

Arguments

DATA1

The original data set (1)

DATA2

The original data set (2)

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

Value

The bootstrap critical values

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).

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

# NOT RUN {
data(sys.risk) 

## sample size
T = nrow(sys.risk)

## matrix for quantile regressions
## - 1st column: dependent variables
## - the rest:   regressors or predictors 
D1 = cbind(sys.risk[2:T,"Market"], sys.risk[1:(T-1),"Market"])
D2 = cbind(sys.risk[2:T,"JPM"], sys.risk[1:(T-1),"JPM"])

## probability levels
vecA = c(0.1, 0.2)

## 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 to 5, after quantile regression 
Qstat.reg.sb(D1, D2, vecA, 5, gamma, Bsize, sigLev)

# }