Nothing
##' The partial cross-quantilograms from 1 to a given lag order.
##'
##' This function calculates the partial cross-quantilograms up to the lag order
##' users specify.
##' @title Partial Corss-Quantilogram upto a given lag order
##' @param DATA1 An input matrix (T x p1)
##' @param DATA2 An input matrix (T x p2)
##' @param vecA A vector of probability values at which sample quantiles are estimated
##' @param Kmax The maximum lag order (integer)
##' @return A vector of cross-quantilogram and a vector of partial cross-quantilograms
##'
##' @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."
##' \emph{Journal of Econometrics}, 193(1), 251-270.
##'
##' @author Heejoon Han, Oliver Linton, Tatsushi Oka and Yoon-Jae Whang
##' @export
crossqreg.max.partial = function(DATA1, DATA2, vecA, Kmax)
{
## Quantile Hit process with demean
matH = qreg.hit(DATA1, DATA2, vecA)
## (3) for each lag
vecCRQ = matrix(0, Kmax, 1)
vecParCRQ = matrix(0, Kmax, 1)
for (k in 1:Kmax){
## cross-quantilogram of lag order k
RES = corr.lag.partial(matH, k)
vecCRQ[k] = RES$CRQ
vecParCRQ[k] = RES$ParCRQ
}
## list
list(CRQ = vecCRQ, ParCRQ = vecParCRQ)
} ## EoF
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