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interventional_analysis_2.R
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#intervention analysis 2
setwd("C:/Users/Anton/Documents/Education/DataMining/Project")
#setwd("C:/Users/Anton Yeshchenko/Desktop/Data mining/project")
library(dplyr)
library(tidyr)
library(ggplot2)
library(reshape)
library(scales)
library(forecast)
require(hydroGOF)
data <- read.csv("Indicators.csv")
# European Union 2004 Members
Slovenia = subset(data, CountryCode == "SVN" & Year >= 1995)
Slovakia = subset(data, CountryCode == "SVK" & Year >= 1995)
Poland = subset(data, CountryCode == "POL" & Year >= 1995)
Malta = subset(data, CountryCode == "MLT" & Year >= 1995)
Lithuania = subset(data, CountryCode == "LTU" & Year >= 1995)
Latvia = subset(data, CountryCode == "LVA" & Year >= 1995)
Hungary = subset(data, CountryCode == "HUN" & Year >= 1995)
Estonia = subset(data, CountryCode == "EST" & Year >= 1995)
CzechRepublic = subset(data, CountryCode == "CZE" & Year >= 1995)
Cyprus = subset(data, CountryCode == "CYP" & Year >= 1995)
#To compare with
Croatia = subset(data, CountryCode == "HRV" & Year >= 1995)
Romania = subset(data, CountryCode == "ROM" & Year >= 1995)
Bulgaria = subset(data, CountryCode == "BGR" & Year >= 1995)
# GDP
GDP.Slovenia = subset(Slovenia, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Slovakia = subset(Slovakia, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Poland = subset(Poland, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Malta = subset(Malta, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Lithuania = subset(Lithuania, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Latvia = subset(Latvia, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Hungary = subset(Hungary, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Estonia = subset(Estonia, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.CzechRepublic = subset(CzechRepublic, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Cyprus = subset(Cyprus, IndicatorCode == "NY.GDP.PCAP.CD")
## to compare with
GDP.Croatia = subset(Croatia, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Romania = subset(Romania, IndicatorCode == "NY.GDP.PCAP.CD")
GDP.Bulgaria = subset(Bulgaria, IndicatorCode == "NY.GDP.PCAP.CD")
# Consumer Price Index
CPI.Slovenia = subset(Slovenia, IndicatorCode == "FP.CPI.TOTL")
CPI.Slovakia = subset(Slovakia, IndicatorCode == "FP.CPI.TOTL")
CPI.Poland = subset(Poland, IndicatorCode == "FP.CPI.TOTL")
CPI.Malta = subset(Malta, IndicatorCode == "FP.CPI.TOTL")
CPI.Lithuania = subset(Lithuania, IndicatorCode == "FP.CPI.TOTL")
CPI.Latvia = subset(Latvia, IndicatorCode == "FP.CPI.TOTL")
CPI.Hungary = subset(Hungary, IndicatorCode == "FP.CPI.TOTL")
CPI.Estonia = subset(Estonia, IndicatorCode == "FP.CPI.TOTL")
CPI.CzechRepublic = subset(CzechRepublic, IndicatorCode == "FP.CPI.TOTL")
CPI.Cyprus = subset(Cyprus, IndicatorCode == "FP.CPI.TOTL")
## to compare with
CPI.Croatia = subset(Croatia, IndicatorCode == "FP.CPI.TOTL")
CPI.Romania = subset(Romania, IndicatorCode == "FP.CPI.TOTL")
CPI.Bulgaria = subset(Bulgaria, IndicatorCode == "FP.CPI.TOTL")
# Unemployment
UEmploy.Slovenia = subset(Slovenia, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Slovakia = subset(Slovakia, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Poland = subset(Poland, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Malta = subset(Malta, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Lithuania = subset(Lithuania, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Latvia = subset(Latvia, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Hungary = subset(Hungary, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Estonia = subset(Estonia, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.CzechRepublic = subset(CzechRepublic, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Cyprus = subset(Cyprus, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
## to compare with
UEmploy.Croatia = subset(Croatia, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Romania = subset(Romania, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
UEmploy.Bulgaria = subset(Bulgaria, IndicatorCode == "SL.UEM.TOTL.NE.ZS")
# Trade
Trade.Slovenia = subset(Slovenia, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Slovakia = subset(Slovakia, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Poland = subset(Poland, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Malta = subset(Malta, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Lithuania = subset(Lithuania, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Latvia = subset(Latvia, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Hungary = subset(Hungary, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Estonia = subset(Estonia, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.CzechRepublic = subset(CzechRepublic, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Cyprus = subset(Cyprus, IndicatorCode == "NE.TRD.GNFS.ZS")
## to compare with
Trade.Croatia = subset(Croatia, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Romania = subset(Romania, IndicatorCode == "NE.TRD.GNFS.ZS")
Trade.Bulgaria = subset(Bulgaria, IndicatorCode == "NE.TRD.GNFS.ZS")
# Savings
HealthExpenditure.Slovenia = subset(Slovenia, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Slovakia = subset(Slovakia, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Poland = subset(Poland, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Malta = subset(Malta, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Lithuania = subset(Lithuania, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Latvia = subset(Latvia, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Hungary = subset(Hungary, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Estonia = subset(Estonia, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.CzechRepublic = subset(CzechRepublic, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Cyprus = subset(Cyprus, IndicatorCode == "SH.XPD.PCAP")
## to compare with
HealthExpenditure.Croatia = subset(Croatia, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Romania = subset(Romania, IndicatorCode == "SH.XPD.PCAP")
HealthExpenditure.Bulgaria = subset(Bulgaria, IndicatorCode == "SH.XPD.PCAP")
countriesEU = c("SVN", "SVK", "POL", "MLT", "LTU", "LVA", "HUN", "EST", "CZE", "CYP")
indicators =c("NY.GDP.PCAP.CD", "FP.CPI.TOTL.ZG", "SL.UEM.TOTL.NE.ZS", "NE.CON.PRVT.PP.CD", "NE.TRD.GNFS.ZS")
####################################################################################
#################
################ Additional functions
################
####################################################################################
#these are to find autocorrelation and autovariance to ensure that the process in not stationary
getInterventionCoeficient <- function(fit, val){
pr <- predict(fit, n.ahead = 5)
val <- values_indicator_2004_2008(val)
interv = sum((val - pr$pred)/pr$pred)
return(interv)
}
getInterventionCoeficientABS <- function(fit, val){
pr <- predict(fit, n.ahead = 5)
val <- values_indicator_2004_2008(val)
interv = abs(sum((val - pr$pred)/pr$pred))
return(interv)
}
#this one for reverse situation (when we expect ressession)
getInterventionCoeficient_res <- function(fit, val){
pr <- predict(fit, n.ahead = 5)
pr$pred[pr$pred < 0] = 0
val <- values_indicator_2004_2008(val)
interv = sum((pr$pred - val)/val)
return(interv)
}
getGrowthInterventionVector <- function(fit, val){
pr <- predict(fit, n.ahead = 5)
val <- values_indicator_2004_2008(val)
return(val - pr$pred)
}
#
getGrowthInterventionVector_res <- function(fit, val){
pr <- predict(fit, n.ahead = 5)
pr$pred[pr$pred < 0] = 0
val <- values_indicator_2004_2008(val)
return(pr$pred - val)
}
plotPredictedVsReal <- function(startYear, endYear, intervYear, fit, val, color_real = "blue", color_pred = "green"){
pr <- predict(fit, n.ahead = (endYear-intervYear + 1))
pr$pred[pr$pred < 0] = 0
mat <- data.frame(year=startYear:endYear, value=val)
mat_pred <- data.frame(year = (intervYear):endYear, value = pr$pred)
colnames(mat_pred) <- c("year", "value")
colnames(mat) <- c("year", "value")
ggplot()+
geom_line(data=mat, aes(year,value), size=2, color=color_real)+
geom_line(data=mat_pred, aes(year,value),size=2, color=color_pred)
}
first_year <- function(indicator) {
return(indicator[1,5])
}
last_year <- function(indicator) {
return(indicator[nrow(indicator),5])
}
missing_present <- function(indicator){
return(sum(is.na(indicator[,6])))
}
values_indicator <- function(indicator){
return(indicator[,6])
}
values_indicator_2004_2008 <- function(indicator){
ind <- subset(indicator, Year <= 2008 & Year >= 2004)
return(ind[,6])
}
values_indicator_before_intervention <- function(indicator, intervention = 2004){
return(subset(indicator, Year < 2004)[,6])
}
####################################################################################
#################
################ Additional functions END END END END
################
####################################################################################
Countr <- c("Poland",
"Slovenia" ,
"Slovakia" ,
"Lithuania",
"Latvia",
"Hungary",
"Estonia",
"CzechRepublic" ,
"Cyprus",
"Malta","Croatia","Romania", "Bulgaria")
NumberOfCountries = 13
Yea <- c(2004:2008)
#this is overall coefficients for every country
K_PRED = matrix(nrow = 5,ncol = NumberOfCountries)
colnames(K_PRED) <- Countr
rownames(K_PRED) <- c("GDP","CPI", "Unemployment", "Trade", "HealthExpenditure")
K_PRED <- as.data.frame(K_PRED)
####################################################################################
#################
#Start with GDP
GDP.Slovenia
GDP.Slovakia
GDP.Poland
GDP.Malta
GDP.Lithuania
GDP.Latvia
GDP.Hungary
GDP.Estonia
GDP.CzechRepublic
GDP.Cyprus
GDP_PRED = matrix(nrow = 5,ncol = NumberOfCountries)
rownames(GDP_PRED) <- Yea
colnames(GDP_PRED) <- Countr
GDP_PRED <- as.data.frame(GDP_PRED)
PLOTG_NICE = matrix(nrow = 108, ncol = 4)
colnames(PLOTG_NICE) <- c("id", "order", "Year", "Value")
PLOTG_NICE <- as.data.frame(PLOTG_NICE)
####################################### POLAND
missing_present(GDP.Poland)
first_year(GDP.Poland)
last_year(GDP.Poland)
auto.arima(values_indicator_before_intervention(GDP.Poland))
fit <- Arima(values_indicator_before_intervention(GDP.Poland), order=c(3,3,0))
plotPredictedVsReal(first_year(GDP.Poland), last_year(GDP.Poland), 2004, fit,values_indicator(GDP.Poland) )
GDP_PRED$Poland <- getGrowthInterventionVector(fit,GDP.Poland)
K_PRED$Poland[1] <- getInterventionCoeficient(fit,GDP.Poland)
K_PRED
####################
###### FOR PLOT
PLOTG_NICE
N_CURRENT = 0
c = subset(GDP.Poland, Year <= 2010 & Year >= 2000)
for (i in (1+N_CURRENT):(11+N_CURRENT)){
PLOTG_NICE[i,1] = "Poland"
PLOTG_NICE[i,2] = "Actual"
PLOTG_NICE[i,3] = c[i-N_CURRENT,5]
PLOTG_NICE[i,4] = c[i-N_CURRENT,6]
}
N_CURRENT = N_CURRENT + 11
Ppr <- predict(fit, n.ahead = 7)
for (i in (1+N_CURRENT):(7+N_CURRENT)){
PLOTG_NICE[i,1] = "Poland"
PLOTG_NICE[i,2] = "Predicted"
PLOTG_NICE[i,3] = c(2004:2010)[i-N_CURRENT]
PLOTG_NICE[i,4] = Ppr$pred[i-N_CURRENT]
}
N_CURRENT = N_CURRENT + 7
current_coef <- GDP.Slovenia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(1,2,4))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Slovenia <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Slovenia[1] <- getInterventionCoeficient(fit,current_coef)
K_PRED
####################################################################################
###### Slovakia
current_coef <- GDP.Slovakia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(3,1,5))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Slovakia <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Slovakia[1] <- getInterventionCoeficient(fit,current_coef)
K_PRED
####################################################################################
################# Lithuania
current_coef <- GDP.Lithuania
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,1))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Lithuania <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Lithuania[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
c = subset(current_coef, Year <= 2010 & Year >= 2000)
for (i in (1+N_CURRENT):(11+N_CURRENT)){
PLOTG_NICE[i,1] = "Lithuania"
PLOTG_NICE[i,2] = "Actual"
PLOTG_NICE[i,3] = c[i-N_CURRENT,5]
PLOTG_NICE[i,4] = c[i-N_CURRENT,6]
}
N_CURRENT = N_CURRENT + 11
Ppr <- predict(fit, n.ahead = 7)
for (i in (1+N_CURRENT):(7+N_CURRENT)){
PLOTG_NICE[i,1] = "Lithuania"
PLOTG_NICE[i,2] = "Predicted"
PLOTG_NICE[i,3] = c(2004:2010)[i-N_CURRENT]
PLOTG_NICE[i,4] = Ppr$pred[i-N_CURRENT]
}
N_CURRENT = N_CURRENT + 7
####################################################################################
################# Latvia
current_coef <- GDP.Latvia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(0,2,0))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Latvia <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Latvia[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
c = subset(current_coef, Year <= 2010 & Year >= 2000)
for (i in (1+N_CURRENT):(11+N_CURRENT)){
PLOTG_NICE[i,1] = "Latvia"
PLOTG_NICE[i,2] = "Actual"
PLOTG_NICE[i,3] = c[i-N_CURRENT,5]
PLOTG_NICE[i,4] = c[i-N_CURRENT,6]
}
N_CURRENT = N_CURRENT + 11
Ppr <- predict(fit, n.ahead = 7)
for (i in (1+N_CURRENT):(7+N_CURRENT)){
PLOTG_NICE[i,1] = "Latvia"
PLOTG_NICE[i,2] = "Predicted"
PLOTG_NICE[i,3] = c(2004:2010)[i-N_CURRENT]
PLOTG_NICE[i,4] = Ppr$pred[i-N_CURRENT]
}
N_CURRENT = N_CURRENT + 7
####################################################################################
################# Hungary
current_coef <- GDP.Hungary
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,3))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Hungary <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Hungary[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
####################################################################################
################# Estonia
current_coef <- GDP.Estonia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(0,2,0))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Estonia <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Estonia[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
c = subset(current_coef, Year <= 2010 & Year >= 2000)
for (i in (1+N_CURRENT):(11+N_CURRENT)){
PLOTG_NICE[i,1] = "Estonia"
PLOTG_NICE[i,2] = "Actual"
PLOTG_NICE[i,3] = c[i-N_CURRENT,5]
PLOTG_NICE[i,4] = c[i-N_CURRENT,6]
}
N_CURRENT = N_CURRENT + 11
Ppr <- predict(fit, n.ahead = 7)
for (i in (1+N_CURRENT):(7+N_CURRENT)){
PLOTG_NICE[i,1] = "Estonia"
PLOTG_NICE[i,2] = "Predicted"
PLOTG_NICE[i,3] = c(2004:2010)[i-N_CURRENT]
PLOTG_NICE[i,4] = Ppr$pred[i-N_CURRENT]
}
N_CURRENT = N_CURRENT + 7
####################################################################################
#################CzechRepublic
current_coef <- GDP.CzechRepublic
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(1,2,0))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$CzechRepublic <- getGrowthInterventionVector(fit,current_coef)
K_PRED$CzechRepublic[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
####################################################################################
################# Cyprus
current_coef <- GDP.Cyprus
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(4,2,1))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Cyprus <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Cyprus[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
####################################################################################
################# Malta
current_coef <- GDP.Malta
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,0))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Malta <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Malta[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
####################################################################################
################# Croatia
current_coef <- GDP.Croatia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(1,1,0))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Croatia <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Croatia[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
c = subset(current_coef, Year <= 2010 & Year >= 2000)
for (i in (1+N_CURRENT):(11+N_CURRENT)){
PLOTG_NICE[i,1] = "Croatia"
PLOTG_NICE[i,2] = "Actual"
PLOTG_NICE[i,3] = c[i-N_CURRENT,5]
PLOTG_NICE[i,4] = c[i-N_CURRENT,6]
}
N_CURRENT = N_CURRENT + 11
Ppr <- predict(fit, n.ahead = 7)
for (i in (1+N_CURRENT):(7+N_CURRENT)){
PLOTG_NICE[i,1] = "Croatia"
PLOTG_NICE[i,2] = "Predicted"
PLOTG_NICE[i,3] = c(2004:2010)[i-N_CURRENT]
PLOTG_NICE[i,4] = Ppr$pred[i-N_CURRENT]
}
N_CURRENT = N_CURRENT + 7
####################################################################################
################# Romania
current_coef <- GDP.Romania
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,2))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Romania <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Romania[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
####################################################################################
################# Bulgaria
current_coef <- GDP.Bulgaria
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(1,2,0))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
GDP_PRED$Bulgaria <- getGrowthInterventionVector(fit,current_coef)
K_PRED$Bulgaria[1] <- getInterventionCoeficient(fit,current_coef)
GDP_PRED
K_PRED
c = subset(current_coef, Year <= 2010 & Year >= 2000)
for (i in (1+N_CURRENT):(11+N_CURRENT)){
PLOTG_NICE[i,1] = "Bulgaria"
PLOTG_NICE[i,2] = "Actual"
PLOTG_NICE[i,3] = c[i-N_CURRENT,5]
PLOTG_NICE[i,4] = c[i-N_CURRENT,6]
}
N_CURRENT = N_CURRENT + 11
Ppr <- predict(fit, n.ahead = 7)
for (i in (1+N_CURRENT):(7+N_CURRENT)){
PLOTG_NICE[i,1] = "Bulgaria"
PLOTG_NICE[i,2] = "Predicted"
PLOTG_NICE[i,3] = c(2004:2010)[i-N_CURRENT]
PLOTG_NICE[i,4] = Ppr$pred[i-N_CURRENT]
}
N_CURRENT = N_CURRENT + 7
####################################################################################
####################################################################################
####################################################################################
####################################################################################
############ CPI
############ Here we have Consumer price index
############
####################################################################################
####################################################################################
####################################################################################
####################################################################################
CPI_PRED = matrix(nrow = 5,ncol = NumberOfCountries)
rownames(CPI_PRED) <- Yea
colnames(CPI_PRED) <- Countr
CPI_PRED <- as.data.frame(CPI_PRED)
####################################### POLAND
current_coef <- CPI.Poland
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(0,2,0))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Poland <- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Poland[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Slovenia
current_coef <- CPI.Slovenia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(0,2,2))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Slovenia <- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Slovenia[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Slovakia
current_coef <- CPI.Slovakia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(0,2,2))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Slovakia <- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Slovakia[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Lithuania
current_coef <- CPI.Lithuania
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(0,2,2))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Lithuania <- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Lithuania[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Latvia
current_coef <- CPI.Latvia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(1,2,2))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Latvia <- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Latvia[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Hungary
current_coef <- CPI.Hungary
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,2))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Hungary<- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Hungary[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Estonia
current_coef <- CPI.Estonia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(0,2,0))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Estonia<- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Estonia[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# ChechRepublic
current_coef <- CPI.CzechRepublic
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,5))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$CzechRepublic<- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$CzechRepublic[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Cyprus
current_coef <- CPI.Cyprus
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,5))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Cyprus<- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Cyprus[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Malta
current_coef <- CPI.Malta
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,2))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Malta<- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Malta[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Croatia
current_coef <- CPI.Croatia
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,2,1))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Croatia<- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Croatia[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Romania
current_coef <- CPI.Romania
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(1,2,1))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Romania<- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Romania[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
################# Bulgaria
current_coef <- CPI.Bulgaria
missing_present(current_coef)
first_year(current_coef)
last_year(current_coef)
acf(values_indicator_before_intervention(current_coef))
pacf(values_indicator_before_intervention(current_coef))
auto.arima(values_indicator_before_intervention(current_coef))
fit <- Arima(values_indicator_before_intervention(current_coef), order=c(2,1,1))
plotPredictedVsReal(first_year(current_coef), last_year(current_coef), 2004, fit,values_indicator(current_coef) )
CPI_PRED$Bulgaria<- getGrowthInterventionVector_res(fit,current_coef)
K_PRED$Bulgaria[2] <- getInterventionCoeficient_res(fit,current_coef)
CPI_PRED
K_PRED
####################################################################################
####################################################################################
####################################################################################
####################################################################################
############
############ Unemployment
############
####################################################################################
####################################################################################
####################################################################################
####################################################################################
UNEMP_PRED = matrix(nrow = 5,ncol = NumberOfCountries)
rownames(UNEMP_PRED) <- Yea
colnames(UNEMP_PRED) <- Countr
UNEMP_PRED <- as.data.frame(UNEMP_PRED)
####################################################################################
####################################### POLAND