#' @title External Connectedness Measures
#' @description This function provides external connectedness measures
#' @param dca Dynamic connectedness object
#' @param groups List of at least two group vectors
#' @param start Start index
#' @param end End index
#' @return Get connectedness measures
#' @examples
#' \donttest{
#' data("gg2018")
#' dca = ConnectednessApproach(gg2018, model="VAR",
#' connectedness="Time",
#' nlag=1, nfore=10, window.size=200)
#' ext = ExternalConnectedness(dca, groups=list("US"=c(1,2,3,4), "JP"=c(5,6,7,8)))
#' }
#' @references Gabauer, D., & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 171, 63-71.
#' @author David Gabauer
#' @export
ExternalConnectedness = function(dca, groups=list(c(1), c(2:ncol(dca$NET))), start=NULL, end=NULL) {
corrected = dca$config$corrected
if (is.null(start)) {
start = 1
}
if (is.null(end)) {
end = dim(dca$CT)[3]
}
if (length(groups)<=1) {
stop("groups need to consist of at least 2 vectors")
}
if (dca$config$approach=="Joint") {
stop(paste("Decomposed connectedness measures are not implemented for",dca$approach, "connectedness"))
} else if (dca$config$approach=="Frequency") {
ct = dca$CT[,,start:end,]
NAMES = colnames(ct)
mn = dim(ct)[4]
TABLE = list()
horizons = dimnames(ct)[[4]]
k = dim(ct)[2]
ct_inter = ct_wo = ct
date = as.character(dimnames(ct)[[3]])
t = dim(ct)[3]
m = length(groups)
NAMES_group = names(groups)
if (is.null(NAMES_group)) {
NAMES_group = paste0("GROUP", 1:m)
}
for (i in 1:m) {
group_1 = groups[[i]]
ct_wo[group_1,group_1,,] = 0
}
TCI_wo = array(0, c(t,mn), dimnames=list(date,horizons))
FROM_wo = TO_wo = NPT_wo = NET_wo = array(0, c(t,k,mn), dimnames=list(date, NAMES,horizons))
NPDC_wo = PCI_wo = INFLUENCE_wo = array(0, c(k,k,t,mn), dimnames=list(NAMES, NAMES, date,horizons))
for (jl in 2:mn) {
for (i in 1:t) {
dca_ = ConnectednessTable(ct_wo[,,i,jl])
TO_wo[i,,jl] = dca_$TO
FROM_wo[i,,jl] = dca_$FROM
NET_wo[i,,jl] = dca_$NET
NPT_wo[i,,jl] = dca_$NPT
NPDC_wo[,,i,jl] = dca_$NPDC
if (corrected) {
TCI_wo[i,jl] = dca_$cTCI
} else {
TCI_wo[i,jl] = dca_$TCI
}
}
if (corrected) {
m_ = (k-1)
} else {
m_ = k
}
TCI_group = array(NA, c(t,m,mn), dimnames=list(date, NAMES_group, horizons))
for (i in 1:m) {
group = groups[i][[1]]
TCI_group[,i,jl] = rowSums(TO_wo[,group,jl,drop=FALSE])/m_
}
TABLE[[jl]] = ConnectednessTable(ct_wo[,,,jl])$TABLE
}
TABLE[[1]] = ConnectednessTable(ct_wo[,,,1])$TABLE
names(TABLE) = horizons
TCI_wo[,1] = apply(TCI_wo,1,sum)
TO_wo[,,1] = apply(TO_wo,1:2,sum)
FROM_wo[,,1] = apply(FROM_wo,1:2,sum)
NET_wo[,,1] = apply(NET_wo,1:2,sum)
NPDC_wo[,,,1] = apply(NPDC_wo,1:3,sum)
for (jl in length(horizons):1) {
for (ij in 1:t) {
for (i in 1:k) {
for (j in 1:k) {
PCI_wo[i,j,ij,jl] = 200*(ct_wo[i,j,ij,jl]+ct_wo[j,i,ij,jl])/(ct[i,i,ij,1]+ct[i,j,ij,1]+ct[j,i,ij,1]+ct[j,j,ij,1])
}
}
INFLUENCE_wo[,,ij,jl] = abs(NPDC_wo[,,ij,jl]/t(t(ct[,,ij,1])+ct[,,ij,1]))
}
NPT_wo[ij,,jl] = rowSums(NPDC_wo[,,ij,jl]<0)
}
ind = which(is.nan(PCI_wo) | is.infinite(PCI_wo),arr.ind=TRUE)
if (length(ind)>0) {
PCI_wo[ind] = 0
}
ind = which(is.nan(INFLUENCE_wo) | is.infinite(INFLUENCE_wo),arr.ind=TRUE)
if (length(ind)>0) {
INFLUENCE_wo[ind] = 0
}
} else {
approach = dca$config$approach=="Extended Joint"
ct = dca$CT[,,start:end]
NAMES = colnames(ct)
k = dim(ct)[2]
if (length(dim(ct))==2) {
ct = array(ct, c(k,k,1),dimnames=list(NAMES,NAMES))
}
ct_inter = ct_wo = ct
date = as.character(dimnames(ct)[[3]])
t = dim(ct)[3]
m = length(groups)
NAMES_group = names(groups)
if (is.null(NAMES_group)) {
NAMES_group = paste0("GROUP", 1:m)
}
apply(ct_wo,1:2,mean)
for (i in 1:m) {
group_1 = groups[[i]]
ct_wo[group_1,group_1,] = 0
}
TCI_wo = array(NA, c(t, 1), dimnames=list(date, c("TCI")))
INFLUENCE_wo = PCI_wo = NPDC_wo = array(NA, c(k, k, t), dimnames=list(NAMES,NAMES,date))
TO_wo = FROM_wo = NET_wo = NPT_wo = array(NA, c(t, k), dimnames=list(date, NAMES))
for (i in 1:t) {
dca_ = ConnectednessTable(ct_wo[,,i])
TO_wo[i,] = dca_$TO
FROM_wo[i,] = dca_$FROM
NET_wo[i,] = dca_$NET
NPT_wo[i,] = dca_$NPT
NPDC_wo[,,i] = dca_$NPDC
PCI_wo[,,i] = dca_$PCI*0
INFLUENCE_wo[,,i] = dca_$INFLUENCE*0
if (corrected) {
TCI_wo[i,] = dca_$cTCI
} else {
TCI_wo[i,] = dca_$TCI
}
}
if (corrected) {
m_ = (k-1)
} else {
m_ = k
}
for (ij in 1:t) {
for (i in 1:k) {
for (j in 1:k) {
PCI_wo[i,j,ij] = (200/ (2*approach))*(ct_wo[i,j,ij]+ct_wo[j,i,ij])/(ct[i,i,ij]+ct[i,j,ij]+ct[j,i,ij]+ct[j,j,ij])
}
}
INFLUENCE_wo[,,ij] = abs(NPDC_wo[,,ij]/t(t(ct[,,ij])+ct[,,ij]))
}
TCI_group = array(NA, c(t,m), dimnames=list(date, NAMES_group))
for (i in 1:m) {
group = groups[i][[1]]
TCI_group[,i] = rowSums(TO_wo[,group,drop=FALSE])/m_
}
ind = which(is.nan(PCI_wo) | is.infinite(PCI_wo),arr.ind=TRUE)
if (length(ind)>0) {
PCI_wo[ind] = 0
}
ind = which(is.nan(INFLUENCE_wo) | is.infinite(INFLUENCE_wo),arr.ind=TRUE)
if (length(ind)>0) {
INFLUENCE_wo[ind] = 0
}
TABLE = ConnectednessTable(ct_wo)$TABLE
if (approach) {
k = dim(NET_wo)[2]
TABLE[k+2,k+1] = "TCI"
TABLE[k+3,k+1] = format(round(mean(TCI_wo),2), nsmall=2)
}
}
return = list(TABLE=TABLE, gTCI=TCI_group, TCI=TCI_wo, TO=TO_wo, FROM=FROM_wo, NPT=NPT_wo,
NET=NET_wo, NPDC=NPDC_wo, PCI=PCI_wo, INFLUENCE=INFLUENCE_wo, config=list(approach="External"))
}
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