crossqreg: Cross-Quantilogram

Description Usage Arguments Details Value Author(s) References Examples

View source: R/crossqreg.R

Description

Returns the cross-quantilogram

Usage

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crossqreg(DATA1, DATA2, vecA, k)

Arguments

DATA1

An input matrix (T x p1)

DATA2

An input matrix (T x p2)

vecA

A pair of two probability values at which sample quantiles are estimated

k

A lag order (integer)

Details

This function obtains the cross-quantilogram at the k lag order.

Value

Cross-Quantilogram

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.

Koenker, R., and Bassett Jr, G. (1978). "Regression quantiles." Econometrica, 46(1), 33-50.

Examples

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## data source 
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)

## cross-quantilogram with the lag of 5, after quantile regression 
crossqreg(D1, D2, vecA, 5)

quantilogram documentation built on July 1, 2020, 10:26 p.m.