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install.packages("ivmodel")
library(ivmodel)
card=card.data[,-35]
head(card)

#endogenitas
lwage=as.matrix(card[,33])
educ=card[,c(4)]
exper=card[,(32)]
expersq=card[,c(34)]
black=card[,c(22)]
smsa=card[,c(23)]
south=card[,c(24)]

library(lmtest)
model=lm(lwage~educ+exper+expersq+black+smsa+south)
summary(model)

#uji keberadaan tiap variabel
m<-data.frame(card$educ, card$exper, card$expersq, card$black, card$smsa, card$south)
cor(m)

##Uji keberadaan korelasi tiap variabel yang memiliki korelas kuat dengan residual model
m1<-data.frame(card$educ, card$exper, card$expersq, model$residuals)
cor(m1)


##Reduced from schooling#
#educ sebagai y
age=card[,c(25)]
age2=card[,c(5)]^2
black=card[,c(22)]
smsa=card[,c(23)]
south=card[,c(24)]
nearc4=card[,c(3)]
model1=lm(educ~age+age2+black+smsa+south+nearc4)
summary(model1)


#wage equation estimated by IV#
install.packages("ivpack")
library(ivpack)
Y=as.matrix(card[,33])
En<-cbind(schooling=educ,exper,expersq) #variabel endogenik
W<-cbind(black,smsa,south)#variabel eksogenik
z<-cbind(age,age2,nearc4)#variabel instrumen
iv<-ivreg(Y~En+W|W+z)
summary(iv,diagnostics=TRUE)


##Heteroskedastisitas
#menguji adanya ragam residual yang tidak homogen
library(lmtest)
install.packages("skedastic")
library(skedastic)