GDP_GROWTH
vince
12/22/2020
R Markdown
r = getOption("repos")
r["CRAN"] = "http://cran.us.r-project.org"
options(repos = r)all=readxl::excel_sheets("growth.xlsx")
all## [1] "growth" "Trade" "INFDI"
## [4] "OUFDI" "UNEMP" "PUBLIC_EXPENSE"
## [7] "POPULATION" "GDP_DEFLATOR" "PRIVATE_SECTOR"
## [10] "TAX REVENUE" "Access_Electricity" "BROAD_MONEY"growth=readxl::read_excel("growth.xlsx",sheet="growth")
trade=readxl::read_excel("growth.xlsx",sheet="Trade")
infdi=readxl::read_excel("growth.xlsx",sheet="INFDI")
oufdi=readxl::read_excel("growth.xlsx",sheet="OUFDI")
unemp=readxl::read_excel("growth.xlsx",sheet="UNEMP")
pbe=readxl::read_excel("growth.xlsx",sheet="PUBLIC_EXPENSE")
pop=readxl::read_excel("growth.xlsx",sheet="POPULATION")
gdpd=readxl::read_excel("growth.xlsx",sheet="GDP_DEFLATOR")
psect=readxl::read_excel("growth.xlsx",sheet="PRIVATE_SECTOR")
tax=readxl::read_excel("growth.xlsx",sheet="TAX REVENUE")
accelec=readxl::read_excel("growth.xlsx",sheet="Access_Electricity")
bmoney=readxl::read_excel("growth.xlsx",sheet="BROAD_MONEY")head(growth,3)## # A tibble: 3 x 33
## ID `Country Name` `Country Code` `1990` `1991` `1992` `1993` `1994` `1995`
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Aruba ABW 3.96 7.96 5.88 7.31 8.20 2.55
## 2 2 Afghanistan AFG NA NA NA NA NA NA
## 3 3 Angola AGO -3.45 0.991 -5.84 -24.0 1.34 15.0
## # … with 24 more variables: `1996` <dbl>, `1997` <dbl>, `1998` <dbl>,
## # `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, `2002` <dbl>, `2003` <dbl>,
## # `2004` <dbl>, `2005` <dbl>, `2006` <dbl>, `2007` <dbl>, `2008` <dbl>,
## # `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>,
## # `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <dbl>, `2018` <dbl>,
## # `2019` <dbl>Reshape wide to long
#growth1=na.omit(growth)
#head(growth1,4)
growth1=reshape(growth,
direction = "long",
varying = list(names(growth)[4:33]),
v.names = "growth",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.trade1=reshape(trade,
direction = "long",
varying = list(names(trade)[4:33]),
v.names = "trade",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.infdi1=reshape(infdi,
direction = "long",
varying = list(names(infdi)[4:33]),
v.names = "infdi",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.oufdi1=reshape(oufdi,
direction = "long",
varying = list(names(oufdi)[4:33]),
v.names = "oufdi",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.unemp1=reshape(unemp,
direction = "long",
varying = list(names(unemp)[4:33]),
v.names = "unemp",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.psect1=reshape(psect,
direction = "long",
varying = list(names(psect)[4:33]),
v.names = "psect",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.tax1=reshape(tax,
direction = "long",
varying = list(names(tax)[4:33]),
v.names = "tax",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.pbe1=reshape(pbe,
direction = "long",
varying = list(names(pbe)[4:33]),
v.names = "pbe",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.pop1=reshape(pop,
direction = "long",
varying = list(names(pop)[4:33]),
v.names = "pop",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.gdpd1=reshape(gdpd,
direction = "long",
varying = list(names(gdpd)[4:33]),
v.names = "gdpd",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.accelec1=reshape(accelec,
direction = "long",
varying = list(names(growth)[4:33]),
v.names = "accelec",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.bmoney1=reshape(bmoney,
direction = "long",
varying = list(names(bmoney)[4:33]),
v.names = "bmoney",
idvar = c("ID","Country Name", "Country Code"),
timevar = "Year",
times = 1990:2019)## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.
## Warning: Setting row names on a tibble is deprecated.MERGE DATA
#str(growth1)
total1= merge(growth1,trade1,by=c("ID","Country Name","Country Code","Year"))
total2= merge(total1,infdi1,by=c("ID","Country Name","Country Code","Year"))
total3= merge(total2,oufdi1,by=c("ID","Country Name","Country Code","Year"))
total4= merge(total3,unemp1,by=c("ID","Country Name","Country Code","Year"))
total5= merge(total4,psect1,by=c("ID","Country Name","Country Code","Year"))
total6= merge(total5,tax1,by=c("ID","Country Name","Country Code","Year"))
total7= merge(total6,pbe1,by=c("ID","Country Name","Country Code","Year"))
total8= merge(total7,pop1,by=c("ID","Country Name","Country Code","Year"))
total9= merge(total8,gdpd1,by=c("ID","Country Name","Country Code","Year"))
total10= merge(total9,accelec1,by=c("ID","Country Name","Country Code","Year"))
total11= merge(total10,bmoney1,by=c("ID","Country Name","Country Code","Year"))
head(total11)## ID Country Name Country Code Year growth trade infdi oufdi
## 1 1 Aruba ABW 1990 3.961402 NA 17.0617063 63.6553106
## 2 1 Aruba ABW 1991 7.962872 NA 21.1833966 20.3030007
## 3 1 Aruba ABW 1992 5.882353 NA -3.8585979 1.0375082
## 4 1 Aruba ABW 1993 7.307692 NA -1.6558907 0.6345002
## 5 1 Aruba ABW 1994 8.203903 NA -5.8750138 0.1210881
## 6 1 Aruba ABW 1995 2.547144 171.3875 -0.4188437 0.1269223
## unemp psect tax pbe pop gdpd accelec bmoney
## 1 NA 44.48402 NA NA 62149 5.815738 NA 55.59662
## 2 6.08 44.22231 NA NA 64622 5.612127 NA 56.97110
## 3 NA 43.29178 NA NA 68235 3.792575 NA 58.32440
## 4 NA 40.91200 NA NA 72504 5.296524 NA 54.91893
## 5 6.45 42.44904 NA NA 76700 6.303148 NA 53.75696
## 6 NA 43.06822 NA NA 80324 3.370637 NA 53.36272First Analysis
total= dplyr::select(total11,(5:16))
corr=cor(total,use ="complete.obs")
corrplot::corrplot(corr,method='number')
totalx=dplyr::rename(total,GDP.Growth=growth,Trade=trade,FDI.NetInflows=infdi,
FDI.NetOutflows=oufdi,Unemployment=unemp,
Private_Sector=psect,Tax.Revenue=tax,Public.Expense=pbe,
Population=pop,GDP.Deflator=gdpd,Access.Electricity=accelec,Broad.Money=bmoney)
totalxx=na.omit(totalx)
totalxxx=as.data.frame(totalxx)
summary(totalxxx)## GDP.Growth Trade FDI.NetInflows FDI.NetOutflows
## Min. :-21.595 Min. : 15.16 Min. :-41.063 Min. :-43.3725
## 1st Qu.: 1.710 1st Qu.: 50.41 1st Qu.: 1.384 1st Qu.: 0.1442
## Median : 3.246 Median : 69.51 Median : 2.737 Median : 0.7496
## Mean : 3.320 Mean : 78.60 Mean : 3.964 Mean : 1.7900
## 3rd Qu.: 4.947 3rd Qu.: 94.10 3rd Qu.: 4.780 3rd Qu.: 2.1857
## Max. : 26.756 Max. :437.33 Max. :103.337 Max. : 75.9995
## Unemployment Private_Sector Tax.Revenue Public.Expense
## Min. : 0.1413 Min. : 3.59 Min. : 0.04349 Min. : 4.066
## 1st Qu.: 4.4780 1st Qu.: 33.66 1st Qu.: 12.92218 1st Qu.:18.329
## Median : 6.7522 Median : 52.16 Median : 15.44675 Median :25.797
## Mean : 7.8341 Mean : 71.22 Mean : 16.82193 Mean :25.824
## 3rd Qu.: 9.4875 3rd Qu.:106.60 3rd Qu.: 20.49038 3rd Qu.:31.975
## Max. :35.4600 Max. :308.98 Max. :110.18305 Max. :83.616
## Population GDP.Deflator Access.Electricity Broad.Money
## Min. :4.460e+04 Min. : -25.958 Min. : 2.624 Min. : 17.31
## 1st Qu.:5.101e+06 1st Qu.: 1.810 1st Qu.: 95.588 1st Qu.: 44.13
## Median :1.653e+07 Median : 3.503 Median : 99.990 Median : 58.82
## Mean :2.120e+08 Mean : 9.554 Mean : 93.140 Mean : 69.81
## 3rd Qu.:8.552e+07 3rd Qu.: 6.933 3rd Qu.:100.000 3rd Qu.: 87.72
## Max. :7.592e+09 Max. :2700.442 Max. :100.000 Max. :252.92total_plus=dplyr::rename(total,GDP.Growth=growth,Trade=trade,FDI.NetInflows=infdi,
FDI.NetOutflows=oufdi,Unemployment=unemp,
Private_Sector=psect,Tax.Revenue=tax,Public.Expense=pbe,
Population=pop,GDP.Deflator=gdpd,Access.Electricity=accelec,Broad.Money=bmoney)Select Country by region
total_22=dplyr::rename(total11,GDP.Growth=growth,Trade=trade,FDI.NetInflows=infdi,
FDI.NetOutflows=oufdi,Unemployment=unemp,
Private_Sector=psect,Tax.Revenue=tax,Public.Expense=pbe,
Population=pop,GDP.Deflator=gdpd,Access.Electricity=accelec,Broad.Money=bmoney)
region=subset(total_22,`Country Name`=="East Asia & Pacific"|`Country Name`=="Europe & Central Asia"|
`Country Name`=="Latin America & Caribbean"|`Country Name`=="Middle East & North Africa"|
`Country Name`=="Sub-Saharan Africa")
Income=subset(total_22,`Country Name`=="High income"|`Country Name`=="Low income"|
`Country Name`=="Middle income"|`Country Name`=="Upper middle income")library(foreign)
coplot(GDP.Growth ~ Year|`Country Name`, type="l",col="red",data=region, pch=40)
coplot(GDP.Growth ~ Year|`Country Name`, type="l",col="blue", data=Income)
coplot( GDP.Deflator ~ Year|`Country Name`, type="l",col='dark green', data=region)
coplot( GDP.Deflator ~ Year|`Country Name`, type="l",col='dark orange', data=Income)
#print(summary(Income))
#print(summary(region))library(stats)
total_scale=scale(total_plus)
total_scale1=as.data.frame(total_scale)
ols=lm(GDP.Growth ~ Trade + FDI.NetInflows + Unemployment +
Private_Sector + Tax.Revenue + Public.Expense ,data=total_scale1)
summary(ols)##
## Call:
## lm(formula = GDP.Growth ~ Trade + FDI.NetInflows + Unemployment +
## Private_Sector + Tax.Revenue + Public.Expense, data = total_scale1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7648 -0.2293 0.0330 0.2642 5.8290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002135 0.014216 -0.150 0.8806
## Trade 0.062283 0.012253 5.083 4.06e-07 ***
## FDI.NetInflows 0.075756 0.031389 2.413 0.0159 *
## Unemployment -0.108872 0.015564 -6.995 3.62e-12 ***
## Private_Sector -0.128550 0.011519 -11.160 < 2e-16 ***
## Tax.Revenue 0.104580 0.018138 5.766 9.41e-09 ***
## Public.Expense -0.228264 0.019309 -11.822 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5546 on 1969 degrees of freedom
## (5944 observations deleted due to missingness)
## Multiple R-squared: 0.1714, Adjusted R-squared: 0.1688
## F-statistic: 67.86 on 6 and 1969 DF, p-value: < 2.2e-16car::vif(ols)## Trade FDI.NetInflows Unemployment Private_Sector Tax.Revenue
## 1.136152 1.163757 1.111203 1.056231 1.716455
## Public.Expense
## 1.698027ols2=lm(GDP.Growth~Trade+FDI.NetInflows+Unemployment+Private_Sector+
Tax.Revenue +Public.Expense, data=total_scale1)
summary(ols2)##
## Call:
## lm(formula = GDP.Growth ~ Trade + FDI.NetInflows + Unemployment +
## Private_Sector + Tax.Revenue + Public.Expense, data = total_scale1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7648 -0.2293 0.0330 0.2642 5.8290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002135 0.014216 -0.150 0.8806
## Trade 0.062283 0.012253 5.083 4.06e-07 ***
## FDI.NetInflows 0.075756 0.031389 2.413 0.0159 *
## Unemployment -0.108872 0.015564 -6.995 3.62e-12 ***
## Private_Sector -0.128550 0.011519 -11.160 < 2e-16 ***
## Tax.Revenue 0.104580 0.018138 5.766 9.41e-09 ***
## Public.Expense -0.228264 0.019309 -11.822 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5546 on 1969 degrees of freedom
## (5944 observations deleted due to missingness)
## Multiple R-squared: 0.1714, Adjusted R-squared: 0.1688
## F-statistic: 67.86 on 6 and 1969 DF, p-value: < 2.2e-16car::vif(ols2)## Trade FDI.NetInflows Unemployment Private_Sector Tax.Revenue
## 1.136152 1.163757 1.111203 1.056231 1.716455
## Public.Expense
## 1.698027library(olsrr)##
## Attaching package: 'olsrr'## The following object is masked from 'package:datasets':
##
## riversols3=MASS::rlm(GDP.Growth~Trade+FDI.NetInflows+Unemployment+Private_Sector+
Tax.Revenue +Public.Expense, data=total_scale1)
summary(ols3)##
## Call: rlm(formula = GDP.Growth ~ Trade + FDI.NetInflows + Unemployment +
## Private_Sector + Tax.Revenue + Public.Expense, data = total_scale1)
## Residuals:
## Min 1Q Median 3Q Max
## -4.79524 -0.24167 0.01213 0.24180 5.91184
##
## Coefficients:
## Value Std. Error t value
## (Intercept) 0.0123 0.0108 1.1423
## Trade 0.0561 0.0093 6.0312
## FDI.NetInflows 0.0556 0.0238 2.3347
## Unemployment -0.0826 0.0118 -6.9888
## Private_Sector -0.1182 0.0087 -13.5112
## Tax.Revenue 0.1166 0.0138 8.4705
## Public.Expense -0.2428 0.0147 -16.5675
##
## Residual standard error: 0.3585 on 1969 degrees of freedom
## (5944 observations deleted due to missingness)car::vif(ols3)## Trade FDI.NetInflows Unemployment Private_Sector Tax.Revenue
## 1.136152 1.163757 1.111203 1.056231 1.716455
## Public.Expense
## 1.698027high=dplyr::select(total_22,GDP.Growth,Trade,FDI.NetInflows,Unemployment,Private_Sector,
Tax.Revenue,Public.Expense)
GGally::ggpairs(high)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## Warning: Removed 728 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 1426 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 1212 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3923 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 2471 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4194 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4436 rows containing missing values## Warning: Removed 1426 rows containing missing values (geom_point).## Warning: Removed 1341 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 1667 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4149 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 2791 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4364 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4593 rows containing missing values## Warning: Removed 1212 rows containing missing values (geom_point).## Warning: Removed 1667 rows containing missing values (geom_point).## Warning: Removed 1111 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4096 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 2460 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4231 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4464 rows containing missing values## Warning: Removed 3923 rows containing missing values (geom_point).## Warning: Removed 4149 rows containing missing values (geom_point).## Warning: Removed 4096 rows containing missing values (geom_point).## Warning: Removed 3838 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4896 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5186 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5326 rows containing missing values## Warning: Removed 2471 rows containing missing values (geom_point).## Warning: Removed 2791 rows containing missing values (geom_point).## Warning: Removed 2460 rows containing missing values (geom_point).## Warning: Removed 4896 rows containing missing values (geom_point).## Warning: Removed 2403 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4932 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5155 rows containing missing values## Warning: Removed 4194 rows containing missing values (geom_point).## Warning: Removed 4364 rows containing missing values (geom_point).## Warning: Removed 4231 rows containing missing values (geom_point).## Warning: Removed 5186 rows containing missing values (geom_point).## Warning: Removed 4932 rows containing missing values (geom_point).## Warning: Removed 4178 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 4431 rows containing missing values## Warning: Removed 4436 rows containing missing values (geom_point).## Warning: Removed 4593 rows containing missing values (geom_point).## Warning: Removed 4464 rows containing missing values (geom_point).## Warning: Removed 5326 rows containing missing values (geom_point).## Warning: Removed 5155 rows containing missing values (geom_point).## Warning: Removed 4431 rows containing missing values (geom_point).## Warning: Removed 4421 rows containing non-finite values (stat_density).
install.packages('tseries')## Installing package into '/Users/vincenttanoe/Library/R/4.0/library'
## (as 'lib' is unspecified)##
## The downloaded binary packages are in
## /var/folders/sw/m_qbgxpn3pzcgbb04_cl0mxh0000gn/T//RtmpdaBfnN/downloaded_packagesinstall.packages('plm')## Installing package into '/Users/vincenttanoe/Library/R/4.0/library'
## (as 'lib' is unspecified)##
## The downloaded binary packages are in
## /var/folders/sw/m_qbgxpn3pzcgbb04_cl0mxh0000gn/T//RtmpdaBfnN/downloaded_packageslibrary(tseries)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoolibrary(plm)Random effect
total_23=na.omit(total_22)
random <- plm(GDP.Growth ~ Trade + FDI.NetInflows + Unemployment +
Private_Sector + Tax.Revenue + Public.Expense + Public.Expense,
data=total_22, index=c("Country Code", "Year"), model="random")
summary(random)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = GDP.Growth ~ Trade + FDI.NetInflows + Unemployment +
## Private_Sector + Tax.Revenue + Public.Expense + Public.Expense,
## data = total_22, model = "random", index = c("Country Code",
## "Year"))
##
## Unbalanced Panel: n = 150, T = 1-30, N = 1976
##
## Effects:
## var std.dev share
## idiosyncratic 8.244 2.871 0.867
## individual 1.263 1.124 0.133
## theta:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.06881 0.44609 0.48417 0.47486 0.55881 0.57732
##
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -27.974 -1.146 0.195 0.004 1.356 32.187
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 6.6310620 0.3836623 17.2836 < 2.2e-16 ***
## Trade 0.0097386 0.0022027 4.4211 9.819e-06 ***
## FDI.NetInflows 0.0077976 0.0038950 2.0020 0.04529 *
## Unemployment -0.1286126 0.0200284 -6.4215 1.349e-10 ***
## Private_Sector -0.0230004 0.0022903 -10.0427 < 2.2e-16 ***
## Tax.Revenue 0.1365066 0.0176864 7.7182 1.180e-14 ***
## Public.Expense -0.1449152 0.0130493 -11.1052 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 20745
## Residual Sum of Squares: 17130
## R-Squared: 0.17427
## Adj. R-Squared: 0.17175
## Chisq: 346.992 on 6 DF, p-value: < 2.22e-16Fixed effect
fixed <- plm(GDP.Growth ~ Trade + FDI.NetInflows + Unemployment +
Private_Sector + Tax.Revenue + Public.Expense + Public.Expense,
data=total_22, index=c("Country Name", "Year"), model="within")
summary(fixed)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = GDP.Growth ~ Trade + FDI.NetInflows + Unemployment +
## Private_Sector + Tax.Revenue + Public.Expense + Public.Expense,
## data = total_22, model = "within", index = c("Country Name",
## "Year"))
##
## Unbalanced Panel: n = 150, T = 1-30, N = 1976
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -27.15939 -1.03481 0.10827 1.27146 29.94624
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## Trade 0.0304084 0.0048579 6.2595 4.804e-10 ***
## FDI.NetInflows 0.0127019 0.0042442 2.9928 0.002802 **
## Unemployment -0.1576514 0.0291623 -5.4060 7.295e-08 ***
## Private_Sector -0.0344033 0.0034734 -9.9049 < 2.2e-16 ***
## Tax.Revenue 0.2193848 0.0254099 8.6338 < 2.2e-16 ***
## Public.Expense -0.2260035 0.0212379 -10.6415 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 18074
## Residual Sum of Squares: 15005
## R-Squared: 0.16981
## Adj. R-Squared: 0.099112
## F-statistic: 62.0469 on 6 and 1820 DF, p-value: < 2.22e-16Hausman test
phtest(fixed, random)##
## Hausman Test
##
## data: GDP.Growth ~ Trade + FDI.NetInflows + Unemployment + Private_Sector + ...
## chisq = 100.08, df = 6, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistentinstall.packages('apsrtable')## Installing package into '/Users/vincenttanoe/Library/R/4.0/library'
## (as 'lib' is unspecified)##
## The downloaded binary packages are in
## /var/folders/sw/m_qbgxpn3pzcgbb04_cl0mxh0000gn/T//RtmpdaBfnN/downloaded_packageslibrary(apsrtable)apsrtable(ols,ols2, model.names = c("OLS_general","OLS_correction"))## \begin{table}[!ht]
## \caption{}
## \label{}
## \begin{tabular}{ l D{.}{.}{2}D{.}{.}{2} }
## \hline
## & \multicolumn{ 1 }{ c }{ OLS_general } & \multicolumn{ 1 }{ c }{ OLS_correction } \\ \hline
## % & OLS_general & OLS_correction\\
## (Intercept) & -0.00 & -0.00 \\
## & (0.01) & (0.01) \\
## Trade & 0.06 ^* & 0.06 ^* \\
## & (0.01) & (0.01) \\
## FDI.NetInflows & 0.08 ^* & 0.08 ^* \\
## & (0.03) & (0.03) \\
## Unemployment & -0.11 ^* & -0.11 ^* \\
## & (0.02) & (0.02) \\
## Private\_Sector & -0.13 ^* & -0.13 ^* \\
## & (0.01) & (0.01) \\
## Tax.Revenue & 0.10 ^* & 0.10 ^* \\
## & (0.02) & (0.02) \\
## Public.Expense & -0.23 ^* & -0.23 ^* \\
## & (0.02) & (0.02) \\
## $N$ & 1976 & 1976 \\
## $R^2$ & 0.17 & 0.17 \\
## adj. $R^2$ & 0.17 & 0.17 \\
## Resid. sd & 0.55 & 0.55 \\ \hline
## \multicolumn{3}{l}{\footnotesize{Standard errors in parentheses}}\\
## \multicolumn{3}{l}{\footnotesize{$^*$ indicates significance at $p< 0.05 $}}
## \end{tabular}
## \end{table}total_m=na.omit(total_plus)
library(caret)## Loading required package: lattice## Loading required package: ggplot2#prepare training scheme
control <- trainControl(method="repeatedcv", number=10, repeats=3)
#train the model
model <- train(GDP.Growth~.,data=total_m, preProcess="scale", trControl=control, na.rm=TRUE)
#estimate variable importance
importance <- varImp(model,scale=FALSE)
print(importance)## rf variable importance
##
## Overall
## Public.Expense 2392.6
## Tax.Revenue 1803.9
## GDP.Deflator 1639.3
## Private_Sector 1567.2
## FDI.NetInflows 1476.3
## Population 1356.6
## Unemployment 1344.3
## Broad.Money 1241.7
## Trade 1180.3
## FDI.NetOutflows 1128.3
## Access.Electricity 698.4plot(importance)
summary(total_m)## GDP.Growth Trade FDI.NetInflows FDI.NetOutflows
## Min. :-21.595 Min. : 15.16 Min. :-41.063 Min. :-43.3725
## 1st Qu.: 1.710 1st Qu.: 50.41 1st Qu.: 1.384 1st Qu.: 0.1442
## Median : 3.246 Median : 69.51 Median : 2.737 Median : 0.7496
## Mean : 3.320 Mean : 78.60 Mean : 3.964 Mean : 1.7900
## 3rd Qu.: 4.947 3rd Qu.: 94.10 3rd Qu.: 4.780 3rd Qu.: 2.1857
## Max. : 26.756 Max. :437.33 Max. :103.337 Max. : 75.9995
## Unemployment Private_Sector Tax.Revenue Public.Expense
## Min. : 0.1413 Min. : 3.59 Min. : 0.04349 Min. : 4.066
## 1st Qu.: 4.4780 1st Qu.: 33.66 1st Qu.: 12.92218 1st Qu.:18.329
## Median : 6.7522 Median : 52.16 Median : 15.44675 Median :25.797
## Mean : 7.8341 Mean : 71.22 Mean : 16.82193 Mean :25.824
## 3rd Qu.: 9.4875 3rd Qu.:106.60 3rd Qu.: 20.49038 3rd Qu.:31.975
## Max. :35.4600 Max. :308.98 Max. :110.18305 Max. :83.616
## Population GDP.Deflator Access.Electricity Broad.Money
## Min. :4.460e+04 Min. : -25.958 Min. : 2.624 Min. : 17.31
## 1st Qu.:5.101e+06 1st Qu.: 1.810 1st Qu.: 95.588 1st Qu.: 44.13
## Median :1.653e+07 Median : 3.503 Median : 99.990 Median : 58.82
## Mean :2.120e+08 Mean : 9.554 Mean : 93.140 Mean : 69.81
## 3rd Qu.:8.552e+07 3rd Qu.: 6.933 3rd Qu.:100.000 3rd Qu.: 87.72
## Max. :7.592e+09 Max. :2700.442 Max. :100.000 Max. :252.92fit=brms::brm(GDP.Growth~Trade+FDI.NetInflows+Unemployment+Private_Sector+
Tax.Revenue +Public.Expense, data=total_scale1)## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car## Warning: Rows containing NAs were excluded from the model.## Compiling Stan program...## Trying to compile a simple C file## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Users/vincenttanoe/Library/R/4.0/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/" -I"/Users/vincenttanoe/Library/R/4.0/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1## Start sampling##
## SAMPLING FOR MODEL 'de9bfce9a3b801fee317fa0a9490bff1' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 2.3e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
## Chain 1: Adjust your expectations accordingly!
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## Chain 4:summary(fit)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: GDP.Growth ~ Trade + FDI.NetInflows + Unemployment + Private_Sector + Tax.Revenue + Public.Expense
## Data: total_scale1 (Number of observations: 1976)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.00 0.01 -0.03 0.02 1.00 6266 3359
## Trade 0.06 0.01 0.04 0.09 1.00 6283 3197
## FDI.NetInflows 0.08 0.03 0.01 0.14 1.00 5729 3140
## Unemployment -0.11 0.02 -0.14 -0.08 1.00 5841 3139
## Private_Sector -0.13 0.01 -0.15 -0.11 1.00 5555 2954
## Tax.Revenue 0.11 0.02 0.07 0.14 1.00 4282 3014
## Public.Expense -0.23 0.02 -0.27 -0.19 1.00 4245 3162
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.55 0.01 0.54 0.57 1.00 5478 2794
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).plot(fit)
pp = brms::pp_check(fit)## Using 10 posterior samples for ppc type 'dens_overlay' by default.pp+theme_bw()
#Default Model Predictions
plot(brms::conditional_effects(fit),points=T)
