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210302_FUNCTIONS.R
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.pkgenv <- new.env(parent=emptyenv())
get_search_term_list <- function(geo,prices_vec){
if(geo=="all"){
# Define the price levels that you would like to retrieve the price levels for
searchterm_list <- sapply(prices_vec,function(x) {
paste0(sprintf("%i", x)," euro", # Name
' + ',sprintf("%i", x)," eur" #Ticker
,' + ',sprintf("%i", x)," €" #Symbol
,' + ',sprintf("%i", x),"€" #Symbol
,' + €',sprintf("%i", x)
,' + ',sprintf("%i", x)," ευρώ" # Greek
,' + ',sprintf("%i", x)," euros" # Spanish Portugese
,' + ',sprintf("%i", x)," euroa" # Finish
#,' + ',sprintf("%i", x)," evrov" # Slovenish
# ,' + ',sprintf("%i", x)," eurot" # Estnisch
# ,' + ',sprintf("%i", x)," eiro" # lettisch
# ,' + ',sprintf("%i", x)," ewro" # Maltesisch
# ,' + ',sprintf("%i", x)," eurų" # Litauisch
#," -in -to -en -a -το -em -v" # Exclude exchange rate queries
)
# in: English/Italian/Dutch; to: English/Dutch; en: French; a; Spanish; em: Protugese; v: Slowenisch
# Maltese changes proposi tion for currency exchanges depending on the target currency => disregarded
#
}
)
}else if(geo=="US"){
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Price levels for US ----
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
searchterm_list <- sapply(prices_vec,function(x) {
paste0(sprintf("%i", x)," $" #Ticker
,' + ',sprintf("%i", x)," dollar" #Ticker
,' + ',sprintf("%i", x),"USD" #Ticker
,' + ',sprintf("%i", x),"$" #Ticker
,' + $',sprintf("%i", x)
,' + ',sprintf("%i", x), ' USD'
,' -million', ' -billion'
)
}
)
}else if(geo=="DE" | geo=="FR" | geo=="LU"){
# Define the price levels that you would like to retrieve the price levels for
searchterm_list <- sapply(prices_vec,function(x) {
paste0(sprintf("%i", x)," euro", # Name
' + ',sprintf("%i", x)," eur" #Ticker
,' + ',sprintf("%i", x)," €" #Symbol
,' + ',sprintf("%i", x),"€" #Symbol
,' + €',sprintf("%i", x)
,' + ',sprintf("%i", x)," euros" # Spanish Portugese
#,' + ',sprintf("%i", x)," evrov" # Slovenish
# ,' + ',sprintf("%i", x)," eurot" # Estnisch
# ,' + ',sprintf("%i", x)," eiro" # lettisch
# ,' + ',sprintf("%i", x)," ewro" # Maltesisch
# ,' + ',sprintf("%i", x)," eurų" # Litauisch
#," -in -to -en -a -το -em -v" # Exclude exchange rate queries
)
# in: English/Italian/Dutch; to: English/Dutch; en: French; a; Spanish; em: Protugese; v: Slowenisch
# Maltese changes proposition for currency exchanges depending on the target currency => disregarded
#
}
)
}
return(searchterm_list)
}
#########################################################################
get_freq_vars <- function(freq,latestDate){
if(freq==1){
strInterval <- "1min"
gfmt <- "%Y-%m-%dT%H"
fmt <- "%Y-%m-%d %H:%M:%S"
if(earliestDate<as.POSIXct("2015-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")){
earliestDate <- as.POSIXct("2015-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")
}
step <- 4*60
goBackIter <- ceiling(as.numeric(difftime(latestDate,earliestDate,unit="min")/step))
}else if(freq==8){
strInterval <- "8min"
gfmt <- "%Y-%m-%dT%H"
nodig <- "13"
fmt <- "%Y-%m-%d %H:%M:%S"
if(earliestDate<as.POSIXct("2015-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")){
earliestDate <- as.POSIXct("2015-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")
}
step <- 35*60
goBackIter <- ceiling(as.numeric(difftime(latestDate,earliestDate,unit="min")/step))
}else if(freq==16){
strInterval <- "16min"
gfmt <- "%Y-%m-%dT%H"
nodig <- "13"
fmt <- "%Y-%m-%d %H:%M:S"
if(earliestDate<as.POSIXct("2015-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")){
earliestDate <- as.POSIXct("2015-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")
}
step <- 70*60
goBackIter <- ceiling(as.numeric(difftime(latestDate,earliestDate,unit="min")/step))
}else if(freq==60){
strInterval <- "hourly"
gfmt <- "%Y-%m-%dT%H"
nodig <- "13"
fmt <- "%Y-%m-%d %H:%M:%S"
if(earliestDate<as.POSIXct("2015-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")){
earliestDate <- as.POSIXct("2015-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")
}
step <- 7*24*60
goBackIter <- ceiling(as.numeric(difftime(latestDate,earliestDate,unit="min")/step))
}else if(freq==1440){
strInterval <- "daily"
nodig <- "10"
gfmt <- fmt <- "%Y-%m-%d"
if(earliestDate<as.POSIXct("2004-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")){
earliestDate <- as.POSIXct("2004-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")
}
step <- 210*24*60
goBackIter <- ceiling(as.numeric(difftime(latestDate,earliestDate,unit="min")/step))
}else if(freq==10080){
strInterval <- "weekly"
nodig <- "10"
gfmt <- fmt <- "%Y-%m-%d"
if(earliestDate<as.POSIXct("2004-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")){
earliestDate <- as.POSIXct("2004-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")
}
step <- 5*365*24*60 - 40*24*60
goBackIter <- ceiling(as.numeric(difftime(latestDate,earliestDate,unit="min")/step))
}else if(freq==43200){
strInterval <- "monthly"
nodig <- "10"
gfmt <- fmt <- "%Y-%m-%d"
if(earliestDate<as.POSIXct("2004-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")){
earliestDate <- as.POSIXct("2004-01-01T00:00:00",format="%Y-%m-%dT%H:%M:%S",tz="UCT")
}
step <- 1
goBackIter <- 1
}else{
stop("Not a valid frequency.")
}
return(list(step=step,goBackIter=goBackIter,gfmt=gfmt,fmt=fmt,nodig=nodig,strInterval=strInterval))
}
#########################################################################
create_outputfolders <- function(dataDIR,strInterval,geo,prop){
# Create the output folders
dir.create(file.path(dataDIR, strInterval), showWarnings = FALSE)
outDIR <- paste0(dataDIR,strInterval,"/")
dir.create(file.path(outDIR, prop), showWarnings = FALSE)
outDIR <- paste0(outDIR,prop,"/")
dir.create(file.path(outDIR, geo), showWarnings = FALSE)
outDIR <- paste0(outDIR,geo,"/")
dir.create(file.path(outDIR, "raw"), showWarnings = FALSE)
return(outDIR)
}
#########################################################################
remove_raw_data <- function(uselocal,query,outDIR){
if(!uselocal){
# Remove all raw data
searchstr <- gsub("([\\+\\$])","\\\\\\1",paste0(query,collapse="#"))
flist <- list.files(path=paste0(outDIR,"raw"),full.names=T)
if(length(query)==1){
flist <- flist[!grepl("#",flist)]
}
do.call(file.remove, list(flist[grepl(searchstr,flist)]))
# Remove all merged data data
flist <- list.files(path=paste0(outDIR),full.names=T)
if(length(query)==1){
flist <- flist[!grepl("#",flist)]
}
do.call(file.remove, list(flist[grepl(searchstr,flist)]))
}
}
#########################################################################
# DOWNLOAD DATA
#########################################################################
download_gtrends <- function(intervalstr,query,region,prop,tz,uselocal){
library("gtrendsR")
# Get new results
attempt <- 1
tmpres <- NULL
restartedTOR <- FALSE
# Try 5 times then give up
while( is.null(tmpres) && attempt <= 5 && (!restartedTOR) ) {
attempt <- attempt + 1
if((attempt == 5) && (!restartedTOR)){
tor$kill()
tor <<- process$new(command="tor")
Sys.sleep(10)
restartedTOR <- TRUE
attempt <- 1
}
if(!uselocal){
###################################################
account <- sample(c(1,2,3,4),1) # THE NUMBER OF YOUR GOOGLE ACCOUNTS GOES HERE
username <- # THE VECTOR OF YOUR GOOGLE ACCOUNTS GOES HERE
passwd <- # THE VECTOR OF THE PASSWORDS FOR YOUR GOOGLE ACCOUNTS GOES HERE
###################################################
gtrendsR::setHandleParameters(user = username[account],
password = passwd[account],
proxyhost = "socks5://127.0.0.1",
proxyport = 9050)
}
try(
tmpres <- gtrends(query,
time = rep(intervalstr,length(query)),
geo=rep(region,length(query)) ,
gprop=prop,
tz = tz,
onlyInterest=TRUE)$interest_over_time
)
}
if(is.null(tmpres)){return(NULL)}
if(length(query)>1){
res <- zoo(as.numeric(tmpres[tmpres$keyword==query[1],2]),order.by = as.POSIXct(tmpres[tmpres$keyword==query[1],1],tz="UCT"))
for(jj in 2:length(query)){
res <- merge(res,zoo(as.numeric(tmpres[tmpres$keyword==query[jj],2]),order.by = as.POSIXct(tmpres[tmpres$keyword==query[jj],1],tz="UCT")))
}
}else{
res <- zoo(as.numeric(tmpres[,2]),order.by = as.POSIXct(tmpres[,1],tz="UCT"))
}
return(res)
}
#########################################################################
# CHECK OVERLAP
#########################################################################
check_overlap <- function(query,tmp,limit,slimit,length.query,redo,uselocal){
tmp <- as.data.frame(tmp)
tmp <- na.fill0(tmp,0)
# There are three possible states
# 001: High intensity gtrends: Use RBC
# 002: Low intensity gtrends: Enlarge sample --> Download again
# 003: Low intensity gtrends: Simple concatenation
# 004: Low intensity gtrends: RBC concatenation
length.query <- length(query)
if(length.query>1){
# Store the columns that belong to the query and the compare level in one row
# First row is always the query, second row is always the comparison level
query.cols <- sapply(1:length.query,function(x) seq(x,ncol(tmp),length.query))
# We require 'slimit' dates with nonzero values in either of the two time frames
number_zeros_lt_slimit <- sapply(query.cols[1,],function(x) (sum(tmp[,x]==0)>slimit))
number_zeros_lt_slimit <- sum(number_zeros_lt_slimit)>1
}else{
query.cols <- matrix(1:2,2,1)
# We require 'slimit' dates with nonzero values in either of the two time frames
number_zeros_lt_slimit <- sapply(query.cols,function(x) (sum(tmp[,x]==0)>slimit))
number_zeros_lt_slimit <- sum(number_zeros_lt_slimit)>1
# if we have number_zeros_lt_slimit==TRUE then we are in a low intensity case
}
# We require 'limit' dates with nonzero values in both time frames
number_zeros_lt_limit <- sum(apply(tmp,1,sum)==0)>limit
# if we have number_zeros_lt_limit==TRUE then we are in a low intensity case
# Have we already redone everything once?
# -- Yes, we have redone it.
if(redo|uselocal){
if((number_zeros_lt_slimit | number_zeros_lt_limit)){
return("003")
}else{
return("001")
}
# -- No, we have not redone it.
}else{
#Low intensity?
# -- Yes, low intensity.
if((number_zeros_lt_slimit | number_zeros_lt_limit)){
return("002")
# -- No, high intensity.
}else{
return("001")
}
}
}
#########################################################################
# REGRESSION
#########################################################################
do.regression <- function(tmp_endres_merge,endres,res,state,testid,siglevel,meanAlgo,query,searchterm_list){
if(length(tmp_endres_merge)!=1){
if(length(query)==1){ # This is the case when we only want to download one time series
X <- cbind(rep(1,NROW(tmp_endres_merge)),as.matrix(tmp_endres_merge[,2]))
Y <- as.matrix(tmp_endres_merge[,1])
na.X <- which(X[,2]==0)
na.Y <- which(Y==0)
if((length(na.X)!=0)|(length(na.Y)!=0)){
X <- X[-union(na.X,na.Y),]
Y <- Y[-union(na.X,na.Y)]
}
if(length(Y)<25){
meanAlgo<-TRUE
}
all.constant.X <- sum(sapply(c(X[,2]),function(x) x==as.numeric(X[1,2])))==length(X[,2])
all.zero.Y <- sum(Y==0) == length(Y)
# In this object we collect the fitted values
Yhat <- matrix(0,NROW(res),1)
if (all.constant.X|meanAlgo){
test <- as.data.frame(tmp_endres_merge)
test[test==0] <- NA
# If all X are constant, then we use the ratio of the means
mean.vec <- apply(test,2,mean,na.rm=T)
if(mean.vec[2]!=0){
ratio <- mean.vec[1]/mean.vec[2]
}else{
ratio <- 1
}
# Predict the NAs in the non-overlapping area (basically with the mean)
Yhat <- as.matrix(res)*ratio
}
else if(all.zero.Y){
Yhat <- 0
}
else{
nn <- nrow(X)
XXinv <- solve(t(X)%*%X)
beta <- XXinv%*%t(X)%*%Y
# Calculate the errors
u <- as.vector(Y-X%*%beta)
# Calculate the heteroskedasticity consistent variance estimator
sigma2 <- XXinv%*%(t(X)%*%diag(u^2)%*%X)%*%t(XXinv)
if(state=="003"){
# Test whether beta_0 > 1.5 (one-sided)
test_alpha1 <- sqrt(nn)*(beta[1])/sqrt(sigma2[1,1])
p.val1 <- 2*min(pt(test_alpha1,nn-2,lower.tail = T),pt(test_alpha1,nn-2,lower.tail = F))
# Calculate R^2
Yhat1 <- X%*%beta
R2 <- 1-sum((Y-Yhat1)^2)/(sum((Y-mean(Y))^2))
if (p.val1<siglevel){
# If we are in a low intensity case, then we use the ratio of the means of the entire
# samples
mean.vec <- c(mean(ifelse(endres==0,NA,endres),na.rm=T),mean(ifelse(res==0,NA,res),na.rm=T))
if(mean.vec[2]!=0){
ratio <- mean.vec[1]/mean.vec[2]
}else{
ratio <- 1
}
# Predict the NAs in the non-overlapping area (basically with the mean)
Yhat <- as.matrix(res)*ratio
}
}
else{
if (testid=="exalpha"){
# Calculate the test statistic
test_alpha <- sqrt(nn)*(beta[1]-1)/sqrt(sigma2[1,1])
p.val <- 2*min(pt(test_alpha,nn-2,lower.tail = T),pt(test_alpha,nn-2,lower.tail = F))
if (T){#} ((p.val<siglevel)){
#If the tests cannot be rejected
# then exclude the constant
XXinv <- solve(t(X[,2])%*%X[,2])
beta <- XXinv%*%t(X[,2])%*%Y
beta <- c(0,beta)
}
}
Xmat <- cbind(rep(1,NROW(res)),as.vector(res))
Yhat <- Xmat%*%beta
}
}
final <- merge(endres,cbind(res,Yhat)[,2])
attr(index(final),"tzone") <- "UTC"
final[is.na(final[,1]),1] <- final[is.na(final[,1]),2]
return(final[,1])
}
else{
# This is the case when we want to download multiple time series
comp.col <- which(query==searchterm_list[which(prices_vec==compare_level)])
plev.col <- which(query!=searchterm_list[which(prices_vec==compare_level)])
query.cols <- sapply(1:length(query),function(x) seq(x,length(query)*2,length(query)))
# Comparison level
X.comp <- cbind(rep(1,NROW(tmp_endres_merge)),as.matrix(tmp_endres_merge[,query.cols[2,comp.col]]))
X.comp[is.infinite(X.comp)] <- 0
Y.comp <- as.matrix(tmp_endres_merge[,query.cols[1,comp.col]])
Y.comp[is.infinite(Y.comp)] <- 0
# Specific price level
X.plev <- cbind(rep(1,NROW(tmp_endres_merge)),as.matrix(tmp_endres_merge[,query.cols[2,plev.col]]))
X.plev[is.infinite(X.plev)] <- 0
Y.plev <- as.matrix(tmp_endres_merge[,query.cols[1,plev.col]])
Y.plev[is.infinite(Y.plev)] <- 0
na.X.plev <- which(X.plev[,2]==0)
na.Y.plev <- which(Y.plev==0)
if((length(na.X.plev)!=0)|(length(na.Y.plev)!=0)){
X.plev <- as.matrix(X.plev[-union(na.X.plev,na.Y.plev),])
Y.plev <- as.matrix(Y.plev[-union(na.X.plev,na.Y.plev)])
}
na.X.comp <- which(X.comp[,2]==0)
na.Y.comp <- which(Y.comp==0)
if((length(na.X.comp)!=0)|(length(na.Y.comp)!=0)){
X.comp <- as.matrix(X.comp[-union(na.X.comp,na.Y.comp),])
Y.comp <- as.matrix(Y.comp[-union(na.X.comp,na.Y.comp)])
}
if(nrow(X.plev)<15){
meanAlgo <- TRUE
}
if(nrow(X.comp)<15){
meanAlgo <- TRUE
}
#
if(!meanAlgo){
# Check if one of the X time series is constant
all.constant.X.comp <- sum(sapply(c(X.comp[,2]),function(x) x==as.numeric(X.comp[1,2])))==length(X.comp[,2])
all.constant.X.plev <- sum(sapply(c(X.plev[,2]),function(x) x==as.numeric(X.plev[1,2])))==length(X.plev[,2])
all.zero.Y.comp <- sum(Y.comp==0) == length(Y.comp)
all.zero.Y.plev <- sum(Y.plev==0) == length(Y.plev)
}else{
all.constant.X.comp <- all.constant.X.plev <- all.zero.Y.comp <- all.zero.Y.plev <- T
}
# In this object we collect the fitted values
Yhat <- matrix(0,NROW(res),NCOL(res))
# First, treat the price level
if (all.constant.X.plev|meanAlgo){
test <- as.data.frame(tmp_endres_merge[,query.cols[,comp.col]])
test[test==0] <- NA
# If all X are constant, then we use the ratio of the means
mean.vec <- apply(test,2,mean,na.rm=T)
if(mean.vec[2]!=0){
ratio <- mean.vec[1]/mean.vec[2]
}else{
ratio <- 1
}
# Predict the NAs in the non-overlapping area (basically with the mean)
Yhat[,query.cols[1,plev.col]] <- as.matrix(res[,query.cols[1,plev.col]])*ratio
}
else if(all.zero.Y.plev){
Yhat[,query.cols[1,plev.col]] <- mean(res[,query.cols[1,plev.col]])
}
else{
nn.plev <- nrow(X.plev)
XXinv.plev <- solve(t(X.plev)%*%X.plev)
beta.plev <- XXinv.plev%*%t(X.plev)%*%Y.plev
# Calculate the errors
u.plev <- as.vector(Y.plev-X.plev%*%beta.plev)
# Calculate the heteroskedasty consistent variance estimator
sigma2.plev <- XXinv.plev%*%(t(X.plev)%*%diag(u.plev^2)%*%X.plev)%*%t(XXinv.plev)
if(state=="003"){
# Test whether beta_0 > 1.5 (one-sided)
test_alpha1.plev <- sqrt(nn.plev)*(beta.plev[1]-1.5)/sqrt(sigma2.plev[1,1])
# Calculate R^2
Yhat1.plev <- X.plev%*%beta.plev
if((sum((Y.plev-mean(Y.plev))^2))==0){
R2.plev <- 1
}else{
R2.plev <- 1-sum((Y.plev-Yhat1.plev)^2)/(sum((Y.plev-mean(Y.plev))^2))
}
if ((R2.plev<0.1) && (test_alpha1.plev<qt(1-siglevel,nn.plev-2))){
# Create full dataset with NAs on non-overlapping dates
final <- cbind(endres,res)
# Select price level time series
final <- final[,query.cols[,plev.col]]
# If all X are constant, then we use the ratio of the means
mean.vec <- apply(final,2,mean,na.rm=T)
if(mean.vec[2]!=0){
ratio <- mean.vec[1]/mean.vec[2]
}else{
ratio <- 1
}
# Predict the NAs in the non-overlapping area (basically with the mean)
Yhat[,query.cols[1,plev.col]] <- as.matrix(res[,query.cols[1,plev.col]])*ratio
}
}
else{
if (testid=="exalpha"){
# Calculate the test statistic
if(sigma2.plev[1,1]==0){#Handle the pathological case, when all values are the same but one -- in X or Y
test_alpha.plev <- 1000
}else{
test_alpha.plev <- sqrt(nn.plev)*(beta.plev[1])/sqrt(sigma2.plev[1,1])
}
p.val.plev <- 2*min(
pt(test_alpha.plev,nn.plev-2,lower.tail = T),
pt(test_alpha.plev,nn.plev-2,lower.tail = F)
)
if ((p.val.plev<siglevel)){
#If the tests can be rejected
# then exclude the constant because then it is to big
XXinv.plev <- solve(t(X.plev[,2])%*%X.plev[,2])
beta.plev <- XXinv.plev%*%t(X.plev[,2])%*%Y.plev
beta.plev <- c(0,beta.plev)
}
}
# Predict the NAs in the non-overlapping area (basically with the mean)
X.plev1 <- cbind(rep(1,NROW(res)),as.matrix(res[,query.cols[1,plev.col]]))
Yhat[,query.cols[1,plev.col]] <- X.plev1%*%beta.plev
}
}
if (all.constant.X.comp|meanAlgo){
test <- as.data.frame(tmp_endres_merge[,query.cols[,comp.col]])
test[test==0] <- NA
# If all X are constant, then we use the ratio of the means
mean.vec <- apply(test,2,mean,na.rm=T)
ratio <- mean.vec[1]/mean.vec[2]
# Predict the NAs in the non-overlapping area (basically with the mean)
Yhat[,query.cols[1,comp.col]] <- as.matrix(res[,query.cols[1,comp.col]])*ratio
}
else if(all.zero.Y.comp){
Yhat[,query.cols[1,comp.col]] <- mean(res[,query.cols[1,comp.col]])
}
else{
nn.comp <- nrow(X.comp)
XXinv.comp <- solve(t(X.comp)%*%X.comp)
beta.comp <- XXinv.comp%*%t(X.comp)%*%Y.comp
# Calculate the errors
u.comp <- as.vector(Y.comp-X.comp%*%beta.comp)
# Calculate the heteroskedasty consistent variance estimator
sigma2.comp <- XXinv.comp%*%(t(X.comp)%*%diag(u.comp^2)%*%X.comp)%*%t(XXinv.comp)
if(state=="003"){
# Test whether beta_0 > 1.5 (one-sided)
test_alpha1.comp <- sqrt(nn.comp)*(beta.comp[1]-1.5)/sqrt(sigma2.comp[1,1])
# Calculate R^2
Yhat1.comp <- X.comp%*%beta.comp
R2.comp <- 1-sum((Y.comp-Yhat1.comp)^2)/(sum((Y.comp-mean(Y.comp))^2))
if ((R2.comp<0.1) && (test_alpha1.comp<qt(1-siglevel,nn.comp-2))){
# Create full dataset with NAs on non-overlapping dates
final <- cbind(endres,res)
# Select comparison level time series
final <- final[,query.cols[,comp.col]]
# If all X are constant, then we use the ratio of the means
mean.vec <- apply(final,2,mean,na.rm=T)
if(mean.vec[2]!=0){
ratio <- mean.vec[1]/mean.vec[2]
}else{
ratio <- 1
}
# Predict the NAs in the non-overlapping area (basically with the mean)
Yhat[,query.cols[1,comp.col]] <- as.matrix(res[,query.cols[1,comp.col]])*ratio
}
}
if (testid=="exalpha"){
# Calculate the test statistic
if(sigma2.comp[1,1]==0){#Handle the pathological case, when all values are the same but one -- in X or Y
test_alpha.comp <- 1000
}else{
test_alpha.comp <- sqrt(nn.comp)*(beta.comp[1])/sqrt(sigma2.comp[1,1])
}
p.val.comp <- 2*min(
pt(test_alpha.comp,nn.comp-2,lower.tail = T),
pt(test_alpha.comp,nn.comp-2,lower.tail = F)
)
if ((p.val.comp<siglevel)){
#If the tests cannot be rejected
# then exclude the constant
XXinv.comp <- solve(t(X.comp[,2])%*%X.comp[,2])
beta.comp <- XXinv.comp%*%t(X.comp[,2])%*%Y.comp
beta.comp <- c(0,beta.comp)
}
}
# Predict the NAs in the non-overlapping area (basically with the mean)
X.comp1 <- cbind(rep(1,NROW(res)),as.matrix(res[,query.cols[1,plev.col]]))
Yhat[,query.cols[1,comp.col]] <- X.comp1%*%beta.comp
}
Yhat <- zoo(Yhat, order.by=index(res))
tmp2 <- cbind(res,Yhat)
final <- merge(endres,tmp2[,(length(query)+1):NCOL(tmp2)])
attr(index(final),"tzone") <- "UTC"
final[is.na(final[,1]),1:length(query)] <- final[is.na(final[,1]),(length(query)+1):NCOL(tmp2)]
endres <- final[,1:length(query)]
}
return(endres)
}
}
#############################################
# Main Function
############################################
main_loop <- function(geo_list,
freq_list,
tz,
searchterm_list = NULL,
prices_vec = NULL,
slimit,
limit,
prop,
compare_level = NULL,
latestDate,
earliestDate,
uselocal,
dataDIR,
overlap,
meanAlgo,
download_comparison_level
){
if((!is.null(prices_vec))&(is.null(compare_level))){
stop("You need a comparison level, if you supply prices")
}
for(geo in geo_list){
#geo <- "US"
#geo <- "DE"
if(!is.null(prices_vec)){
searchterm_list <- get_search_term_list(geo,prices_vec)
}
# searchterm_list <- c("unemployment benefit")#get_search_term_list(geo)
# searchterm_list <- c("DAX")#get_search_term_list(geo)
for(freq in freq_list){
#freq <- freq_list[1]
if((freq == 60)|
(prop == "news")){
limit <- 15
slimit <- 10
}
for (qq in searchterm_list){
if(download_comparison_level){
if(which(qq==searchterm_list)<2){next}
}
#qq <- searchterm_list[1]
method = "backward"
testid = "exalpha"
if(!is.null(prices_vec)){
if (qq==searchterm_list[which(prices_vec==compare_level)]){
query <- qq
}else{
query <- c(qq,searchterm_list[which(prices_vec==compare_level)])
}
}
else{
query <- qq
}
print(paste0("Do the dance for ",query))
region <- geo
if(geo=="all"){
region <- ""
}
# Get the frequency specific variables
freq_vars <- get_freq_vars(freq, latestDate)
step <- freq_vars$step
goBackIter <- freq_vars$goBackIter
gfmt <- freq_vars$gfmt
fmt <- freq_vars$fmt
nodig <- freq_vars$nodig
strInterval <- freq_vars$strInterval
# Create OUTPUT FOLDERS
outDIR <- create_outputfolders(dataDIR,strInterval,geo,prop)
# Remove raw data if uselocal is TRUE
rc <- remove_raw_data(uselocal=uselocal,
query=query,
outDIR=outDIR)
#########################################################################
# PREPARE FIRST ITERATION
#########################################################################
start_date <- end_date <- NA
end_date <- latestDate
# set start and end_date for first iteration
if(freq!=43200){
start_date <- end_date -step*60
}else{
start_date <- earliestDate
}
start_date <- as.POSIXct(start_date,origin="1970-01-01",tz="UCT")
end_date <- as.POSIXct(end_date,origin="1970-01-01",tz="UCT")
endres <- res <- tmp_endres_merge <- NA
pathology <- last <- redo <- FALSE
####################################################################
####################################################################
if(!uselocal){
uselocal <- FALSE
while(TRUE){
#Convert data to an amicable format
intervalstr <- make_intervalstr(start_date,end_date,freq)
# Save old results for next iteration
# but only when we do NOT have a pathological case
# and have to redo the download process
if(!redo){
resold <- res
old_enddate <- end_date
}
# DOWNLOAD DATA
res <- download_gtrends(intervalstr=intervalstr,query=query,region=region,prop=prop,tz=tz,uselocal=uselocal)
names(res) <- NULL
if(is.null(res)){break}
# Merge new results ONLY beginning in the second iteration
if(length(resold)==1){
endres <- res
# 1. Dump the downloaded data
outstr <- gsub(" ","_",intervalstr)
write.zoo(res,file=paste0(outDIR,"raw/",paste0(query,collapse="#"),"_",outstr,".csv"))
# Create new end_date
end_date <- start_date+overlap*freq*60
# Create new start_Date
start_date <- end_date-step*60
}else{
# Merge between two subsequent time frames
tmp <- na.omit(merge(resold,res))
attr(index(tmp),"tzone") <- "UTC"
names(tmp) <- NULL
# Construct the merge for the endresult
if(length(endres)!=1){
tmp_endres_merge <- na.omit(merge(endres,res))
attr(index(tmp_endres_merge),"tzone") <- "UTC"
names(tmp_endres_merge) <- NULL
}else{
tmp_endres_merge <- NA
endres <- res
}
# Merge the samples together
tmp_endres_merge <- as.data.frame(tmp_endres_merge)
if(nrow(tmp_endres_merge)==0){break}
names(tmp_endres_merge) <- c(sapply(1:length(query),function(x) paste0(c("Y","X"),x)))
# Determine how to proceed, by checking the overlap
state <- check_overlap(query=query,
tmp=tmp_endres_merge,
limit=limit,
slimit=slimit,
length.query = length(query),
redo=redo,
uselocal=uselocal)
# There are three possible states
# 001: High intensity gtrends: Use RBC
# 002: Low intensity gtrends: Enlarge sample --> Download again
# 003: Low intensity gtrends: Sample enlarged --> Decide what to do based on regression
if(state %in% c("001","003")) {
redo <- FALSE
}else if(state=="002"){
redo <- TRUE
}
# Adjust start and end dates
if(redo){
end_date <- end_date+overlap*freq*60
start_date <- end_date-step*60
}else{
end_date <- start_date+overlap*freq*60
start_date <- end_date-step*60
}
if(!redo){
########################################################################
# In case everything is fine ...
########################################################################
# 1. Dump the downloaded data
outstr <- gsub(" ","_",intervalstr)
write.zoo(res,file=paste0(outDIR,"raw/",paste0(query,collapse="#"),"_",outstr,".csv"))
# 2. Do the regression and fitting
endres <- do.regression(tmp_endres_merge=tmp_endres_merge,
endres=endres,
res=res,
state=state,
siglevel=siglevel,
testid=testid,
meanAlgo=meanAlgo,
query=query,
searchterm_list)
names(endres) <- NULL
}
if(end_date <= earliestDate){
break
}
if (last){
break
}
if(freq==43200){
break
}
if(start_date <= earliestDate){
start_date <- earliestDate
end_date <- start_date + step*60
last <- TRUE
}
}
}# ENDE MAIN LOOP
# Dump the merged data
write.zoo(endres,file=paste0(outDIR,paste0(query,collapse="#"),"_",method,"_",testid,".csv"))
rm("resold")
rm("endres")
}
else{
uselocal <- TRUE
####################################
# Concatenate backward (exalpha) LOCAL!!!
####################################
method = "backward"
testid = "exalpha"
searchstr <- gsub("([\\+\\$])","\\\\\\1",paste0(query,collapse="#"))
flist <- list.files(path=paste0(outDIR,"/raw"))
flist <- flist[grepl(paste0("^",searchstr),flist)]
dates <- gsub(paste0("^",searchstr,"_(.{",nodig,"})_.*$"),"\\1",flist)
if (length(dates)==0){
next
}
if(length(query)==1){
dates <- dates[!grepl("#",dates)]
dates <- dates[order(as.POSIXct(dates,format=gfmt))]
fname <- flist[grepl(dates[length(dates)],flist)&!grepl("#",flist)]
}
else{
dates <- dates[order(as.POSIXct(dates,format=gfmt))]
fname <- flist[grepl(dates[length(dates)],flist)]
}
endres <- read.table(paste0(outDIR,"raw/",fname),skip=2)
endres <- zoo(as.matrix(endres[,2:(length(query)+1)]),order.by=as.POSIXct(endres[,1],format=fmt,tz ="UCT"))
if(length(dates)>=2){
for(jj in (length(dates)-1):1){
#jj <- length(dates)-1
if(length(query)==1){
fname <- flist[grepl(dates[jj],flist)&!grepl("#",flist)]
}else{
fname <- flist[grepl(dates[jj],flist)]
}
res <- read.table(paste0(outDIR,"raw/",fname),skip=2)
res <- zoo(as.matrix(res[,2:(length(query)+1)]),order.by=as.POSIXct(res[,1],format=fmt,tz ="UCT"))
tmp_endres_merge <- na.omit(merge(endres,res))
attr(index(tmp_endres_merge),"tzone") <- "UTC"
if(nrow(tmp_endres_merge)==0){break}
if(length(tmp_endres_merge)==0){break}
# Determine how to proceed, by checking the overlap
state <- check_overlap(query=query,
tmp=tmp_endres_merge,
limit=limit,
slimit=slimit,
length.query = length(query),
redo=T,
uselocal=uselocal)
endres <- do.regression(tmp_endres_merge=tmp_endres_merge,
endres=endres,
res=res,
state=state,
siglevel=siglevel,
testid=testid,
meanAlgo=meanAlgo,
query=query,
searchterm_list)
}
}
# Dump the merged data
write.zoo(endres,file=paste0(outDIR,paste0(query,collapse="#"),"_",method,"_",testid,".csv"))
rm("endres")
}
if(freq!=43200){
####################################
# Concatenate backward (inalpha)
####################################
method = "backward"
testid = "inalpha"
searchstr <- gsub("([\\+\\$])","\\\\\\1",paste0(query,collapse="#"))
flist <- list.files(path=paste0(outDIR,"/raw"))
flist <- flist[grepl(paste0("^",searchstr),flist)]
dates <- gsub(paste0("^",searchstr,"_(.{",nodig,"})_.*$"),"\\1",flist)
if(length(query)==1){
dates <- dates[!grepl("#",dates)]
dates <- dates[order(as.POSIXct(dates,format=gfmt))]
fname <- flist[grepl(dates[length(dates)],flist)&!grepl("#",flist)]
}else{
dates <- dates[order(as.POSIXct(dates,format=gfmt))]