quantilogram (version 2.0.1)

crossqreg.sb: Stationary Bootstrap for the Cross-Quantilogram

Description

Returns critical values for the cross-quantilogram, based on the stationary bootstrap.

Usage

crossqreg.sb(DATA1, DATA2, vecA, k, gamma, Bsize, sigLev)

Arguments

DATA1

The original data matrix (T x p1)

DATA2

The original data matrix (T x p2)

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

Value

The boostrap critical values

Details

This function generates critical values for for the cross-quantilogram, using the stationary bootstrap in 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. "The stationary bootstrap." Journal of the American Statistical Association 89.428 (1994): 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 10 for test 
sigLev = 0.05 ## significance level

## cross-quantilogram with the lag of 5, after quantile regression 
crossqreg.sb(D1, D2, vecA, 5, gamma, Bsize, sigLev)

# }