library(readr)
fdi <- read_csv("~/Zim Projects PHD/Felix Shayamano/fdi.csv")
## Parsed with column specification:
## cols(
##   YEAR = col_double(),
##   DOMESTIC_INVESTMENTS = col_double(),
##   EMPLOYMENT = col_double(),
##   ECONOMIC_GROWTH = col_double(),
##   TRADE_OPENNES = col_double(),
##   EXTERNAL_DEBT = col_double(),
##   EXPORTS = col_double(),
##   INFLATION = col_double(),
##   GDP = col_double(),
##   FDI = col_double(),
##   HDI = col_double()
## )
View(fdi)
attach(fdi)
names(fdi)
##  [1] "YEAR"                 "DOMESTIC_INVESTMENTS" "EMPLOYMENT"          
##  [4] "ECONOMIC_GROWTH"      "TRADE_OPENNES"        "EXTERNAL_DEBT"       
##  [7] "EXPORTS"              "INFLATION"            "GDP"                 
## [10] "FDI"                  "HDI"
library(lubridate) # for working with dates
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date
library(ggplot2)  # for creating graphs
library(scales)   # to access breaks/formatting functions
## 
## Attaching package: 'scales'
## The following object is masked from 'package:readr':
## 
##     col_factor
library(gridExtra) # for arranging plots
library(ggthemes)
# plot FDI
fd <- ggplot(fdi,aes(x=YEAR,y=FDI)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Foreign Direct Investiment in Zimbabwe") +
           xlab("Year") + ylab("FDI")+theme_economist()

# render the plot


trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

HDI

# plot HDI
fd <- ggplot(fdi,aes(x=YEAR,y=HDI)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("HDI in Zimbabwe") +
           xlab("Year") + ylab("HDI")+theme_economist()

# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

GDP

# plot GDP
fd <- ggplot(fdi,aes(x=YEAR,y=GDP)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Gross Domestic Product  in Zimbabwe") +
           xlab("Year") + ylab("GDP")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

INFLATION

fd <- ggplot(fdi,aes(x=YEAR,y=INFLATION)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Inflation Rate  in Zimbabwe") +
           xlab("Year") + ylab("INFLATION")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

EXPORTS

fd <- ggplot(fdi,aes(x=YEAR,y=EXPORTS)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Exports Rate  in Zimbabwe") +
           xlab("Year") + ylab("EXPORTS")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

EXTERNAL_DEBT

fd <- ggplot(fdi,aes(x=YEAR,y=EXTERNAL_DEBT)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("External Debt  in Zimbabwe") +
           xlab("Year") + ylab("EXTERNAL DEBT")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

TRADE_OPENNES

fd <- ggplot(fdi,aes(x=YEAR,y=TRADE_OPENNES)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Trade Opennes  in Zimbabwe") +
           xlab("Year") + ylab("TRADE OPENNES")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ECONOMIC_GROWTH

fd <- ggplot(fdi,aes(x=YEAR,y=ECONOMIC_GROWTH)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Economic Growth  in Zimbabwe") +
           xlab("Year") + ylab("ECONOMIC GROWTH")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

EMPLOYMENT

fd <- ggplot(fdi,aes(x=YEAR,y=EMPLOYMENT)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Employment Rate in Zimbabwe") +
           xlab("Year") + ylab("EMPLOYMENT")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

DOMESTIC_INVESTMENTS

fd <- ggplot(fdi,aes(x=YEAR,y=DOMESTIC_INVESTMENTS)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Domestic Investiments in Zimbabwe") +
           xlab("Year") + ylab("DOMESTIC INVESTMENTS")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="green")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

One to One plots

FDI and Economic Growth

fd <- ggplot(fdi,aes(x=ECONOMIC_GROWTH,y=FDI)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Economic Growth and Foreign Direct Investiment") +
           xlab("ECONOMIC GROWTH") + ylab("FDI")+theme_economist()
# render the plot

trend <- fd+ stat_smooth(colour="red")

trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

fd <- ggplot(fdi,aes(x=EMPLOYMENT,y=FDI)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Employment Status and Foreign Direct Investiment") +
           xlab("EMPLOYMENT") + ylab("FDI")+theme_economist()
# render the plot
trend <- fd+ stat_smooth(colour="red")
trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

fd <- ggplot(fdi,aes(x=DOMESTIC_INVESTMENTS,y=FDI)) +
           geom_point(na.rm=TRUE, color="purple", size=2) + 
           ggtitle("Domestic Investiments and Foreign Direct Investiment") +
           xlab("DOMESTIC INVESTMENTS") + ylab("FDI")+theme_economist()
# render the plot
trend <- fd+ stat_smooth(colour="red")
trend
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Time series analys

acf(log(FDI))

acf(diff(log(FDI)))

pacf(diff(log(FDI)))

EMPLOYMENT

acf(log(EMPLOYMENT))

acf(diff(log(EMPLOYMENT)))

pacf(diff(log(EMPLOYMENT)))

ECONOMIC_GROWTH

acf(ECONOMIC_GROWTH)

acf(diff(ECONOMIC_GROWTH))

pacf(diff(ECONOMIC_GROWTH))

DOMESTIC_INVESTMENTS

acf(log(DOMESTIC_INVESTMENTS))

acf(diff(log(DOMESTIC_INVESTMENTS)))

pacf(diff(log(DOMESTIC_INVESTMENTS)))

TRADE_OPENNES

acf(log(TRADE_OPENNES))

acf(diff(log(TRADE_OPENNES)))

pacf(diff(log(TRADE_OPENNES)))

EXTERNAL_DEBT

acf(log(EXTERNAL_DEBT))

acf(diff(log(EXTERNAL_DEBT)))

pacf(diff(log(EXTERNAL_DEBT)))

INFLATION

acf(INFLATION)

acf(diff(INFLATION))

pacf(diff(INFLATION))

GDP

acf(GDP)

acf(diff(GDP))

pacf(diff(GDP))

HDI

acf(log(HDI))

acf(diff(log(HDI)))

pacf(diff(log(HDI)))

(fit <- arima(log(FDI), c(0, 1, 1),seasonal = list(order = c(0, 1, 1), period = 12)))
## 
## Call:
## arima(x = log(FDI), order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1), 
##     period = 12))
## 
## Coefficients:
##           ma1     sma1
##       -0.2272   0.0000
## s.e.   0.5210  65.8373
## 
## sigma^2 estimated as 2.497:  log likelihood = -13.16,  aic = 32.32
fit
## 
## Call:
## arima(x = log(FDI), order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1), 
##     period = 12))
## 
## Coefficients:
##           ma1     sma1
##       -0.2272   0.0000
## s.e.   0.5210  65.8373
## 
## sigma^2 estimated as 2.497:  log likelihood = -13.16,  aic = 32.32
#pred <- predict(fit, n.ahead = 10*12)
#ts.plot(FDI,2.718^pred$pred, log = "y", lty = c(1,3))