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Code_projet_ANE_BENNACEUR.R
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Code_projet_ANE_BENNACEUR.R
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setwd("C://Users//benoit//Desktop//ENS//ENSAE//2A//S2//Econo 2//projet")
#########
#Package#
#########
library(haven)
library(utils)
library(ggplot2)
library(stargazer)
library(Hmisc)
library(dplyr)
library(plyr)
library(reshape2)
library(readr)
library(lfe)
library(gmm)
library(pscl)
#################
#Chargement Base#
#################
MAGANewsData <- read.csv("C:/Users/benoit/Desktop/ENS/ENSAE/2A/S2/Econo 2/projet/MAGANewsData.csv")
#Vérification de l'unicité du tuple ('state','town','county') dans la base
# ie : chaque ville apparait bien une seule fois
nrow(unique(MAGANewsData[,c('state','town','county')])) == nrow(MAGANewsData) #youpi
#On drop s'il y a des NA dans une de ses 3 colonnes
MAGANewsDataSNA <- subset(MAGANewsData, (!is.na(MAGANewsData$maganews1998)) & (!is.na(MAGANewsData$maganews2000) & (!is.na(MAGANewsData$maganews2003))))
nrow(MAGANewsDataSNA)/nrow(MAGANewsData)
###########################
#PARTIE 1 #
###########################
#Question 1
unique(MAGANewsData$maganews1998)
unique(MAGANewsData$maganews2000)
unique(MAGANewsData$maganews2003)
maga_98<-sum(MAGANewsData$maganews1998,na.rm = TRUE)
maga_00<-sum(MAGANewsData$maganews2000,na.rm = TRUE)
maga_03<-sum(MAGANewsData$maganews2003,na.rm = TRUE)
maga_98/nrow(MAGANewsData) #6.3%
maga_00/nrow(MAGANewsData) #19.5%
maga_03/nrow(MAGANewsData) #52.3%
na98<-sum(is.na(MAGANewsData$maganews1998))
na03<-sum(is.na(MAGANewsData$maganews2003))
sup_98 <- (maga_98+na98)/nrow(MAGANewsData) #34.22%
sup_03 <- (maga_03+na03)/nrow(MAGANewsData) #58.9%
maga_98_NA<-sum(na.omit(MAGANewsDataSNA$maganews1998))
maga_00_NA<-sum(na.omit(MAGANewsDataSNA$maganews2000))
maga_03_NA<-sum(na.omit(MAGANewsDataSNA$maganews2003))
maga_98_NA/length(na.omit(MAGANewsDataSNA$maganews1998))
maga_00_NA/length(na.omit(MAGANewsDataSNA$maganews2000))
maga_03_NA/length(na.omit(MAGANewsDataSNA$maganews2003))
#Question 2
control <- MAGANewsData[MAGANewsData$maganews2000 == 0,]
treated <- MAGANewsData[MAGANewsData$maganews2000 == 1,]
diffindiff <- (mean(treated$reppresfv2p2000,na.rm = TRUE)-mean(treated$reppresfv2p1996, na.rm = TRUE)) - (mean(control$reppresfv2p2000, na.rm = TRUE)-mean(control$reppresfv2p1996, na.rm = TRUE))
diffindiff
copy <- MAGANewsData[,c("maganews1998","maganews2000","reppresfv2p2000","reppresfv2p1996")]
pane <- rbind(copy[,c("reppresfv2p1996","maganews1998")],setNames(copy[,c("reppresfv2p2000","maganews2000")],c("reppresfv2p1996","maganews1998")))
period <- rep(0,nrow(pane))
period[(nrow(copy)+1):nrow(pane)]<-1
pane["time"] <- period
pane["treated"] <- c(as.numeric(copy$maganews2000 == 1),as.numeric(copy$maganews2000 == 1))
pane["interaction"] <- pane$time * pane$treated
colnames(pane) <- c('propor_vote_rep','maganews','year_2000','treated','interaction')
reg <- lm(propor_vote_rep ~year_2000 + treated + interaction, data=pane)
summary(reg)
stargazer(reg)
#Question 3
mean(control$reppresfv2p1988,na.rm = TRUE)
mean(treated$reppresfv2p1988,na.rm = TRUE)
mean(control$reppresfv2p1992,na.rm = TRUE)
mean(treated$reppresfv2p1992,na.rm = TRUE)
mean(control$reppresfv2p1996,na.rm = TRUE)
mean(treated$reppresfv2p1996,na.rm = TRUE)
year<-c("1988", "1992", "1996")
vote_rep_control<-c(mean(control$reppresfv2p1988,na.rm = TRUE),mean(control$reppresfv2p1992,na.rm = TRUE),mean(control$reppresfv2p1996,na.rm = TRUE) )
vote_rep_treated<-c(mean(treated$reppresfv2p1988,na.rm = TRUE) ,mean(treated$reppresfv2p1992,na.rm = TRUE),mean(treated$reppresfv2p1996,na.rm = TRUE))
df<-data.frame(year,vote_rep_control, vote_rep_treated, stringsAsFactors=FALSE)
plot(df$vote_rep_control, type="l", col="blue", xlab="année", ylab="Part du vote républicain", main="", xaxt="n")+
axis(1, at=seq(1, 3, by=1), labels = c("1988","1992","1996"))
lines(df$vote_rep_treated, col='red')+
legend("topright", lty=c(1,1), col=c("blue","red"), legend = c("Control", "treated"))
sum(is.na(MAGANewsData$reppresfv2p1988))/9256
sum(is.na(MAGANewsData$reppresfv2p1992))/9256
sum(is.na(MAGANewsData$reppresfv2p1996))/9256
#Question 4
mean_sd <- function(base,variable){
return(c(mean(base[[variable]],na.rm = TRUE),sd(base[[variable]],na.rm = TRUE)))
}
mean_sd(control,"college2000")
mean_sd(treated,"college2000")
mean_sd(MAGANewsData,"college2000")
mean_sd(control,"male2000")
mean_sd(treated,"male2000")
mean_sd(MAGANewsData,"male2000")
mean_sd(control,"black2000")
mean_sd(treated,"black2000")
mean_sd(MAGANewsData,"black2000")
mean_sd(control,"hisp2000")
mean_sd(treated,"hisp2000")
mean_sd(MAGANewsData,"hisp2000")
mean_sd(control,"married2000")
mean_sd(treated,"married2000")
mean_sd(MAGANewsData,"married2000")
mean_sd(control,"urban2000")
mean_sd(treated,"urban2000")
mean_sd(MAGANewsData,"urban2000")
mean_sd(control,"unempl2000")
mean_sd(treated,"unempl2000")
mean_sd(MAGANewsData,"unempl2000")
mean_sd(control,"pop2000")
mean_sd(treated,"pop2000")
mean_sd(MAGANewsData,"pop2000")
mean_sd(control,"reppresfv2p2000")
mean_sd(treated,"reppresfv2p2000")
mean_sd(MAGANewsData,"reppresfv2p2000")
###########################
#PARTIE 2 #
###########################
#Question 2
model_1 <- glm(maganews2000 ~ totpreslvpop1996 + reppresfv2p1996,family=binomial(link='logit'),data=MAGANewsData)
summary(model_1)
pR2(model_1)
stargazer(model_1)
model_2 <- glm(maganews2000 ~ totpreslvpop1996 + reppresfv2p1996 +nocable1998+noch1998+sub1998 ,family=binomial(link='logit'),data=MAGANewsDataSNA)
summary(model_2)
pR2(model_2)
stargazer(model_2)
model_3 <- glm(maganews2000 ~ totpreslvpop1996 + reppresfv2p1996 +noch1998+ nocable1998 +college00m90+hs00m90 +unempl00m90+income00m90+sub1998+poptot1998+pop18p2000+black00m90+hisp00m90,family=binomial(link='logit'),data=MAGANewsDataSNA)
summary(model_3)
pR2(model_3)
stargazer(model_3)
#Question 5
g <- function(beta, data) {
#mettre un dataframe avec le traitement D en premier et les variables à prendre ensuite
data<-as.data.frame(data)
d <- as.numeric(data[,1])
x <- data.matrix(data[, 2:ncol(data)])
m <- as.vector((exp(x%*%beta)*(as.vector(1-d))))*as.matrix(x)
return(cbind(m))
}
data_3 <- MAGANewsDataSNA[,c("maganews2000","totpreslvpop1996", "reppresfv2p1996","noch1998", "nocable1998", "college00m90","hs00m90","unempl00m90","income00m90","sub1998","poptot1998","pop18p2000","black00m90","hisp00m90")]
init3 <- (lm(maganews2000 ~ 0+totpreslvpop1996 + reppresfv2p1996 +noch1998+ nocable1998 +college00m90+hs00m90 +unempl00m90+income00m90+sub1998+poptot1998+pop18p2000+black00m90+hisp00m90,data = MAGANewsDataSNA))$coefficients
gmm3 <- gmm(g,na.omit(data_3),t0=init3,type="iterative",itermax = 5000,tol = 1e-15)
summary(gmm3)
stargazer(gmm3)
data_2 <- MAGANewsDataSNA[,c("maganews2000","totpreslvpop1996", "reppresfv2p1996","noch1998", "nocable1998","sub1998")]
init2 <- (lm(maganews2000 ~ 0+totpreslvpop1996 + reppresfv2p1996 +noch1998+ nocable1998 +sub1998,data = MAGANewsDataSNA))$coefficients
gmm2 <- gmm(g,na.omit(data_2),t0=init2,type="iterative",itermax = 5000,tol = 1e-15)
summary(gmm2)
stargazer(gmm2)
data_1 <- MAGANewsDataSNA[,c("maganews2000","totpreslvpop1996", "reppresfv2p1996")]
init1 <- (lm(maganews2000 ~ 0+totpreslvpop1996 + reppresfv2p1996,data = MAGANewsDataSNA))$coefficients
gmm1 <- gmm(g,na.omit(data_1),t0=init1,type="iterative",itermax = 5000,tol = 1e-15)
summary(gmm1)
stargazer(gmm1)
nrow(na.omit(data_3))
nrow(na.omit(data_2))
nrow(na.omit(data_1))
theta_chap <- function(coefs,data){
#maganews2000 en deuxieme colonne
#en premiere colonne il faut la variable reppresfv2p00m96
#puis a partir de la variable 3 c'est les X
data<-as.data.frame(data)
y <- as.numeric(data[,1])
d <- as.numeric(data[,2])
x <- data.matrix(data[,3:ncol(data)])
e <- exp(x%*%coefs)
return((1/sum(d,na.rm = T))*sum(y*(d-(1-d)*e),na.rm = T))
}
data_chap_model3 <- MAGANewsDataSNA[,c("reppresfv2p00m96" ,"maganews2000","totpreslvpop1996", "reppresfv2p1996","noch1998", "nocable1998", "college00m90","hs00m90","unempl00m90","income00m90","sub1998","poptot1998","pop18p2000","black00m90","hisp00m90")]
theta_chap(as.matrix(gmm3$coefficients),na.omit(data_chap_model3)) #estimateur theta chapeau pour le modele 3
data_chap_model2 <- MAGANewsDataSNA[,c("reppresfv2p00m96", "maganews2000","totpreslvpop1996", "reppresfv2p1996","noch1998", "nocable1998","sub1998")]
theta_chap(as.matrix(gmm2$coefficients),na.omit(data_chap_model2)) #estimateur theta chapeau pour le modele 2
data_chap_model1 <- MAGANewsDataSNA[,c("reppresfv2p00m96", "maganews2000","totpreslvpop1996", "reppresfv2p1996")]
theta_chap(as.matrix(gmm1$coefficients),na.omit(data_chap_model1)) #estimateur theta chapeau pour le modele 1
var_theta_chap <- function(coefs,data){
#coefs les coefficients du gmm
#data exactement les memes formats que les data_chap_modeli
#comme pour theta_chap, il faut lancer avec na.omit(data_chap...)
#sinon des NA se mettent dans les matrices et ça explose
data<-as.data.frame(data)
y <- as.numeric(data[,1])
d <- as.numeric(data[,2])
x <- data.matrix(data[,3:ncol(data)])
II <- c()
FF <- c()
U<-c()
for (i in 1:dim(x)[1]){
yi <- y[i]
di <- d[i]
xi <- x[i,]
ei <- exp(xi %*% coefs)
I <- sum(t(xi)%*%xi * (1-di)*ei)
II <- c(II,I)
f<-(di-(1-di)*ei)
u <- yi*(1-di)*as.vector(xi)*ei
U <- c(U,u)
}
EU <- sum(U)/length(U)
EI <- sum(II)/length(II)
EI <- 1/EI
res=c()
for (i in 1:dim(x)[1]){
yi <- y[i]
di <- d[i]
xi <- x[i,]
ei <- exp(xi %*% coefs)
f1i <- (di-(1-di)*ei)
aux <- f1i*(yi-xi*EU*EI)
aux2 <- aux*aux
res <- c(res,aux2)
}
fin <- sum(res)/length(res)
fin <- fin/(mean(d)^2)
return(fin)
}
sd_theta_chap3 <- sqrt(var_theta_chap(as.matrix(gmm3$coefficients),na.omit(data_chap_model3)))
sd_theta_chap2 <- sqrt(var_theta_chap(as.matrix(gmm2$coefficients),na.omit(data_chap_model2)))
sd_theta_chap1 <- sqrt(var_theta_chap(as.matrix(gmm1$coefficients),na.omit(data_chap_model1)))
###################################
#Partie 3 #
###################################
#Question 1
reg_1 <- lm(reppresfv2p00m96 ~ maganews2000+ totpresvotes1996 + reppresfv2p1996 + reppresfv2p96m92 + nocable1998 +college00m90+hs00m90 +unempl00m90+income00m90+sub1998+poptot1998+pop18p2000+black00m90+hisp00m90,data=MAGANewsDataSNA,weights=MAGANewsDataSNA$totpresvotes1996)
summary(reg_1)
stargazer((reg_1))
reg_2 <- felm(reppresfv2p00m96 ~ maganews2000 + totpresvotes1996 + reppresfv2p1996 + reppresfv2p96m92 + nocable1998 +college00m90+hs00m90 +unempl00m90+income00m90+sub1998+poptot1998+pop18p2000+black00m90+hisp00m90 |county, data=MAGANewsDataSNA,weights=MAGANewsDataSNA$totpresvotes1996)
summary(reg_2)
stargazer((reg_2))
###################################
#Partie 4 #
###################################
data_placebo_1 <- MAGANewsDataSNA[,c("county","reppresfv2p96m92","reppresfv2p1996","reppresfv2p1988","totpresvotes1996", "maganews2000","nocable1998","college00m90","hs00m90","unempl00m90","income00m90","sub1998","poptot1998","pop18p2000","black00m90","hisp00m90")]
data_placebo_1 <- na.omit(data_placebo_1)
we<-c(data_placebo_1$totpresvotes1996)
placebo_1 <- felm(reppresfv2p96m92 ~ maganews2000 + totpresvotes1996 + reppresfv2p1996+ reppresfv2p1988 + nocable1998 +college00m90+hs00m90 +unempl00m90+income00m90+sub1998+poptot1998+pop18p2000+black00m90+hisp00m90 | county ,data=data_placebo_1,weights=we)
summary(placebo_1)
stargazer(placebo_1)
data_placebo_2 <- MAGANewsDataSNA[,c("county","reppresfv2p1996","reppresfv2p1988","totpresvotes1996","reppresfv2p92m88", "maganews2000", "nocable1998","college00m90","hs00m90","unempl00m90","income00m90","sub1998","poptot1998","pop18p2000","black00m90","hisp00m90")]
data_placebo_2 <- na.omit(data_placebo_2)
we<-c(data_placebo_2$totpresvotes1996)
placebo_2 <- felm(reppresfv2p92m88 ~ maganews2000 + totpresvotes1996 + reppresfv2p1996 + reppresfv2p1988 + nocable1998 +college00m90+hs00m90 +unempl00m90+income00m90+sub1998+poptot1998+pop18p2000+black00m90+hisp00m90 | county ,data=data_placebo_2,weights=we)
summary(placebo_2)
stargazer(placebo_2)