setwd("H:/MES DOCUMENTS/Mes sites Web/monsiteweb2/LivreEconometrie/programmes/R")
getwd()
library(tseries)
library(car)
library(dynlm)
library(strucchange)
library(mFilter)

## lecture des données
chap5<-read.table("chapitre5R.csv",head=TRUE,sep=";" )
head(chap5)## visualise les premières observations
chap5t<-ts(data = chap5, start = 1970, end = 1998, frequency =4,deltat = 1 )
chap5t[,"pib_fr"]
#Problème n°1 : Evaluation par simulation

## question 1a - creation des variables cpibga glissement annuel du pib


pibga_fr<-100*diff(chap5t[,"pib_fr"], lag=4)/(lag(chap5t[,"pib_fr"],-4))
pibga_dm<-100*diff(chap5t[,"pib_dm"], lag=4)/(lag(chap5t[,"pib_dm"],-4))
pibga_nl<-100*diff(chap5t[,"pib_nl"], lag=4)/(lag(chap5t[,"pib_nl"],-4))
pibga_es<-100*diff(chap5t[,"pib_es"], lag=4)/(lag(chap5t[,"pib_es"],-4))
pibga_it<-100*diff(chap5t[,"pib_it"], lag=4)/(lag(chap5t[,"pib_it"],-4))
pibga_uk<-100*diff(chap5t[,"pib_uk"], lag=4)/(lag(chap5t[,"pib_uk"],-4))


## question 1b - creation des variables ccpibga glissement annuel de cpib
cpiga_fr<-100*diff(chap5t[,"cpi_fr"], lag=4)/(lag(chap5t[,"cpi_fr"],-4))
cpiga_dm<-100*diff(chap5t[,"cpi_dm"], lag=4)/(lag(chap5t[,"cpi_dm"],-4))
cpiga_nl<-100*diff(chap5t[,"cpi_nl"], lag=4)/(lag(chap5t[,"cpi_nl"],-4))
cpiga_es<-100*diff(chap5t[,"cpi_es"], lag=4)/(lag(chap5t[,"cpi_es"],-4))
cpiga_it<-100*diff(chap5t[,"cpi_it"], lag=4)/(lag(chap5t[,"cpi_it"],-4))
cpiga_uk<-100*diff(chap5t[,"cpi_uk"], lag=4)/(lag(chap5t[,"cpi_uk"],-4))

## question 1c
ecinf_fr<-cpiga_fr-2
ecinf_dm<-cpiga_dm-2
ecinf_nl<-cpiga_nl-2
ecinf_es<-cpiga_es-2
ecinf_it<-cpiga_it-2
ecinf_uk<-cpiga_uk-2

hp_fr<-hpfilter(chap5t[,"pib_fr"],freq=1600)
hp_dm<-hpfilter(chap5t[,"pib_dm"],freq=1600)
hpt_nl<-hpfilter(chap5t[29:113,"pib_nl"],freq=1600)
hpt_nl<-hpfilter(chap5t[29:113,"pib_nl"],freq=1600) ## problème des NA
hp_nl<-ts(hpt_nl$trend, start = 1977, end = 1998, frequency =4,deltat=1)
hp_es<-hpfilter(chap5t[,"pib_es"],freq=1600)
hp_it<-hpfilter(chap5t[,"pib_it"],freq=1600)
hp_uk<-hpfilter(chap5t[,"pib_uk"],freq=1600)

hpga_fr<-100*diff(hp_fr$trend, lag=4)/lag(hp_fr$trend,-4)
hpga_dm<-100*diff(hp_dm$trend, lag=4)/lag(hp_dm$trend,-4)
hpga_nl<-100*diff(hp_nl, lag=4)/lag(hp_nl,-4)
hpga_es<-100*diff(hp_es$trend, lag=4)/lag(hp_es$trend,-4)
hpga_it<-100*diff(hp_it$trend, lag=4)/lag(hp_it$trend,-4)
hpga_uk<-100*diff(hp_uk$trend, lag=4)/lag(hp_uk$trend,-4)

gap_fr<-100*(chap5t[,"pib_fr"]-hp_fr$trend)/ hp_fr$trend
gap_dm<-100*(chap5t[,"pib_dm"]-hp_dm$trend)/ hp_dm$trend
gap_nl<-100*(chap5t[,"pib_nl"]-hp_nl)/ hp_nl
gap_es<-100*(chap5t[,"pib_es"]-hp_es$trend)/ hp_es$trend
gap_it<-100*(chap5t[,"pib_it"]-hp_it$trend)/ hp_it$trend
gap_uk<-100*(chap5t[,"pib_uk"]-hp_uk$trend)/ hp_uk$trend

c=c("blue","red")
ts.plot(chap5t[,"pib_dm"],hp_dm$trend,main="allemagne",col=c)
legend("topleft",c("pib_dm","hp_dm"),col=c,lty = c(1,1))
ts.plot(pibga_dm,hpga_dm,main="allemagne",col=c)
legend("topleft",c("pibga_dm","hpga_dm"),,col=c,lty = c(1, 1))
ts.plot(gap_dm,cpiga_dm,main="allemagne",col=c)
legend("topleft",c("gap_dm","gap_dm"),col=c,lty = c(1, 1))


##Question 2
## taux de taylor
ts_fr<-hpga_fr+cpiga_fr+0.5*ecinf_fr+0.5*gap_fr
ts_dm<-hpga_dm+cpiga_dm+0.5*ecinf_dm+0.5*gap_dm
ts_nl<-hpga_nl+cpiga_nl+0.5*ecinf_nl+0.5*gap_nl
ts_es<-hpga_es+cpiga_es+0.5*ecinf_es+0.5*gap_es
ts_it<-hpga_it+cpiga_it+0.5*ecinf_it+0.5*gap_it
ts_uk<-hpga_uk+cpiga_uk+0.5*ecinf_uk+0.5*gap_uk

## taux neutre
tn_fr<-hpga_fr+cpiga_fr
tn_dm<-hpga_dm+cpiga_dm
tn_nl<-hpga_nl+cpiga_nl
tn_es<-hpga_es+cpiga_es
tn_it<-hpga_it+cpiga_it
tn_uk<-hpga_uk+cpiga_uk

c=c("blue","red","black")
ts.plot(chap5t[,"tc_dm"],tn_dm,ts_dm,main="allemagne",col=c)
legend("topright",c("tc_dm","tn_dm","ts_dm"),col=c,lty = c(1,1, 1))


#difference taux neutre taux court
ec_fr = (chap5t[,"tc_fr"]-tn_fr)
ec2_fr = ec_fr**2
ec_dm= (chap5t[,"tc_dm"]-tn_dm)
ec2_dm = ec_dm**2
ec_nl=(chap5t[,"tc_nl"]-tn_nl)
ec2_nl = ec_nl**2
ec_es = (chap5t[,"tc_es"]-tn_es)
ec2_es = ec_es**2
ec_it = (chap5t[,"tc_it"]-tn_it)
ec2_it = ec_it**2
ec_uk = (chap5t[,"tc_uk"]-tn_uk)
ec2_uk = ec_uk**2

# precision de la regle de taylor simulée cas 1
rmsec_fr<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsec_fr[i:i]=sqrt(mean(ec2_fr[i:j]))}


rmsec_dm<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsec_dm[i:i]=sqrt(mean(ec2_dm[i:j]))}
rmsec_dm

rmsec_nl<-ts(start = c(1980,4), end = 1998, frequency =4,deltat=1)
for(i in 1:81){
j=i+11
rmsec_nl[i:i]=sqrt(mean(ec2_nl[i:j]))}
rmsec_nl

rmsec_es<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsec_es[i:i]=sqrt(mean(ec2_es[i:j]))}
rmsec_es

rmsec_it<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsec_it[i:i]=sqrt(mean(ec2_it[i:j]))}
rmsec_it

rmsec_uk<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsec_uk[i:i]=sqrt(mean(ec2_uk[i:j]))}

c=c("black","orange","yellow","blue","red","green")
ts.plot(rmsec_fr,rmsec_dm,rmsec_it,rmsec_uk,rmsec_es,rmsec_nl,
 main="precision de la regle de taylor simulée cas 1",
 col=c)
legend("topright",c("rmsec_fr","rmsec_dm","rmsec_it","rmsec_uk","rmsec_es","rmsec_nl"),
 col=c,lty = c(1,1,1,1,1,1))

#difference taux court et taux de taylor
er2_fr = (chap5t[,"tc_fr"]-ts_fr)**2
er2_dm = (chap5t[,"tc_dm"]-ts_dm)**2
er2_nl = (chap5t[,"tc_nl"]-ts_nl)**2
er2_es = (chap5t[,"tc_es"]-ts_es)**2
er2_it = (chap5t[,"tc_it"]-ts_it)**2
er2_uk = (chap5t[,"tc_uk"]-ts_uk)**2

# precision de la regle de taylor simulée cas2

rmsee_fr<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsee_fr[i:i]=sqrt(mean(er2_fr[i:j]))}
rmsee_fr

rmsee_dm<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsee_dm[i:i]=sqrt(mean(er2_dm[i:j]))}
rmsee_dm

rmsee_nl<-ts(start = c(1980,4), end = 1998, frequency =4,deltat=1)
for(i in 1:81){
j=i+11
rmsee_nl[i:i]=sqrt(mean(er2_nl[i:j]))}
rmsee_nl

rmsee_es<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsee_es[i:i]=sqrt(mean(er2_es[i:j]))}
rmsee_es

rmsee_it<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsee_it[i:i]=sqrt(mean(er2_it[i:j]))}
rmsee_it

rmsee_uk<-ts(start = c(1973,4), end = 1998, frequency =4,deltat=1)
for(i in 1:98){
j=i+11
rmsee_uk[i:i]=sqrt(mean(er2_uk[i:j]))}
rmsee_uk

ts.plot(rmsee_fr,rmsee_dm,rmsee_it,rmsee_uk,rmsee_es,rmsee_nl,
 main="precision de la regle de taylor simulée cas 2",
 col=c)
legend("topright",c("rmsee_fr","rmsee_dm","rmsee_it","rmsee_uk","rmsee_es","rmsee_nl"),
 col=c,lty = c(1,1,1,1,1,1))


#Problème n°2 : Evaluation par estimation et tests sur longue période

reg1_fr=dynlm(ec_fr~ecinf_fr+gap_fr)
summary(reg1_fr)
te_fr<-tn_fr+fitted(reg1_fr)#taux court estimé
te_fr

reg1_dm=dynlm(ec_dm~ecinf_dm+gap_dm)
summary(reg1_dm)
te_dm<-tn_dm+fitted(reg1_dm)#taux court estimé
te_dm

reg1_nl=dynlm(ec_nl~ecinf_nl+gap_nl)
summary(reg1_nl)
te_nl<-tn_nl+fitted(reg1_nl)#taux court estimé
te_nl

reg1_es=dynlm(ec_es~ecinf_es+gap_es)
summary(reg1_es)

te_es<-tn_es+fitted(reg1_es)#taux court estimé
te_es

reg1_it=dynlm(ec_it~ecinf_it+gap_it)
summary(reg1_it)
te_it<-tn_it+fitted(reg1_it)#taux court estimé
te_it

reg1_uk=dynlm(ec_uk~ecinf_uk+gap_uk)
summary(reg1_uk)
te_uk<-tn_uk+fitted(reg1_uk) #taux court estimé
te_uk

#tests sur les paramètres lambda1 et lambda 2
linear.hypothesis(reg1_fr,"ecinf_fr=0.5")
linear.hypothesis(reg1_fr,"gap_fr[5:113]=0.5")
linear.hypothesis(reg1_fr,hypothesis.matrix=c(0,1,0),rhs=0.5)#alternativement
linear.hypothesis(reg1_fr,hypothesis.matrix=c(0,0,1),rhs=0.5)#alternativement

X1<-c(0,0,1,0,0,1)
J<-matrix(data=X1,nrow=2,ncol=3)
X2<-c(0.5,0.5)
Q<-matrix(data=X2,nrow=2,ncol=1)
linear.hypothesis(reg1_fr,hypothesis.matrix=J,rhs=Q)

linear.hypothesis(reg1_dm,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1_dm,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1_dm,hypothesis.matrix=J,rhs=Q)

linear.hypothesis(reg1_nl,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1_nl,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1_nl,hypothesis.matrix=J,rhs=Q)

linear.hypothesis(reg1_es,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1_es,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1_es,hypothesis.matrix=J,rhs=Q)

linear.hypothesis(reg1_it,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1_it,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1_it,hypothesis.matrix=J,rhs=Q)

linear.hypothesis(reg1_uk,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1_uk,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1_uk,hypothesis.matrix=J,rhs=Q)

# Problème n°3 analyse de la stabilité
#question1 estimation par sous-periode
#France
reg1P1_fr=dynlm(ec_fr~ecinf_fr+gap_fr,start="",end=c(1979,4))
linear.hypothesis(reg1P1_fr,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P1_fr,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P1_fr,hypothesis.matrix=J,rhs=Q)
summary(reg1P1_fr)

reg1P2_fr=dynlm(ec_fr~ecinf_fr+gap_fr,start=c(1980,1),end=c(1989,4))
linear.hypothesis(reg1P2_fr,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P2_fr,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P2_fr,hypothesis.matrix=J,rhs=Q)
summary(reg1P2_fr)


reg1P3_fr=dynlm(ec_fr~ecinf_fr+gap_fr,start=c(1990,1))
linear.hypothesis(reg1P3_fr,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P3_fr,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P3_fr,hypothesis.matrix=J,rhs=Q)
summary(reg1P3_fr)

#Allemagne
reg1P1_dm=dynlm(ec_dm~ecinf_dm+gap_dm,start="",end=c(1979,4))
linear.hypothesis(reg1P1_dm,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P1_dm,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P1_dm,hypothesis.matrix=J,rhs=Q)
summary(reg1P1_dm)

reg1P2_dm=dynlm(ec_dm~ecinf_dm+gap_dm,start=c(1980,1),end=c(1989,4))
linear.hypothesis(reg1P2_dm,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P2_dm,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P2_dm,hypothesis.matrix=J,rhs=Q)
summary(reg1P2_dm)

reg1P3_dm=dynlm(ec_dm~ecinf_dm+gap_dm,start=c(1990,1))
linear.hypothesis(reg1P3_dm,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P3_dm,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P3_dm,hypothesis.matrix=J,rhs=Q)
summary(reg1P3_dm)

#pays-Bas
reg1P1_nl=dynlm(ec_nl~ecinf_nl+gap_nl,start="",end=c(1979,4))
linear.hypothesis(reg1P1_nl,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P1_nl,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P1_nl,hypothesis.matrix=J,rhs=Q)
summary(reg1P1_nl)

reg1P2_nl=dynlm(ec_nl~ecinf_nl+gap_nl,start=c(1980,1),end=c(1989,4))
linear.hypothesis(reg1P2_nl,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P2_nl,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P2_nl,hypothesis.matrix=J,rhs=Q)
summary(reg1P2_nl)

reg1P3_nl=dynlm(ec_nl~ecinf_nl+gap_nl,start=c(1990,1))
linear.hypothesis(reg1P3_nl,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P3_nl,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P3_nl,hypothesis.matrix=J,rhs=Q)
summary(reg1P3_nl)


#espagne
reg1P1_es=dynlm(ec_es~ecinf_es+gap_es,start="",end=c(1979,4))
linear.hypothesis(reg1P1_es,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P1_es,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P1_es,hypothesis.matrix=J,rhs=Q)
summary(reg1P1_es)

reg1P2_es=dynlm(ec_es~ecinf_es+gap_es,start=c(1980,1),end=c(1989,4))
linear.hypothesis(reg1P2_es,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P2_es,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P2_es,hypothesis.matrix=J,rhs=Q)
summary(reg1P2_es)

reg1P3_es=dynlm(ec_es~ecinf_es+gap_es,start=c(1990,1))
linear.hypothesis(reg1P3_es,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P3_es,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P3_es,hypothesis.matrix=J,rhs=Q)
summary(reg1P3_es)

#italie
reg1P1_it=dynlm(ec_it~ecinf_it+gap_it,start="",end=c(1979,4))
linear.hypothesis(reg1P1_it,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P1_it,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P1_it,hypothesis.matrix=J,rhs=Q)
summary(reg1P1_it)

reg1P2_it=dynlm(ec_it~ecinf_it+gap_it,start=c(1980,1),end=c(1989,4))
linear.hypothesis(reg1P2_it,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P2_it,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P2_it,hypothesis.matrix=J,rhs=Q)
summary(reg1P2_it)

reg1P3_it=dynlm(ec_it~ecinf_it+gap_it,start=c(1990,1))
linear.hypothesis(reg1P3_it,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P3_it,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P3_it,hypothesis.matrix=J,rhs=Q)
summary(reg1P3_it)

#royaume-uni
reg1P1_uk=dynlm(ec_uk~ecinf_uk+gap_uk,start="",end=c(1979,4))
linear.hypothesis(reg1P1_uk,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P1_uk,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P1_uk,hypothesis.matrix=J,rhs=Q)
summary(reg1P1_uk)

reg1P2_uk=dynlm(ec_uk~ecinf_uk+gap_uk,start=c(1980,1),end=c(1989,4))
linear.hypothesis(reg1P2_uk,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P2_uk,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P2_uk,hypothesis.matrix=J,rhs=Q)
summary(reg1P2_uk)

reg1P3_uk=dynlm(ec_uk~ecinf_uk+gap_uk,start=c(1990,1))
linear.hypothesis(reg1P3_uk,hypothesis.matrix=c(0,1,0),rhs=0.5)
linear.hypothesis(reg1P3_uk,hypothesis.matrix=c(0,0,1),rhs=0.5)
linear.hypothesis(reg1P3_uk,hypothesis.matrix=J,rhs=Q)
summary(reg1P3_uk)

#question 2 test du cusum

wr_fr<-efp(ec_fr~ecinf_fr+gap_fr[5:113],type="Rec-CUSUM")
plot(wr_fr)

wr_dm<-efp(ec_dm~ecinf_dm+gap_dm[5:113],type="Rec-CUSUM")
plot(wr_dm)

wr_nl<-efp(ec_nl~ecinf_nl[29:109]+gap_nl[5:85],type="Rec-CUSUM")
plot(wr_nl)

wr_es<-efp(ec_es[10:109]~ecinf_es[10:109]+gap_es[14:113],type="Rec-CUSUM")
plot(wr_es)

wr_it<-efp(ec_it~ecinf_it+gap_it[5:113],type="Rec-CUSUM")
plot(wr_it)

wr_uk<-efp(ec_uk~ecinf_uk+gap_uk[5:113],type="Rec-CUSUM")
plot(wr_uk)

# question 3 regressions glissantes

#france
lambda1<-vector(length=75)
min1<-vector(length=75)
sup1<-vector(length=75)
lambda2<-vector(length=75)
min2<-vector(length=75)
sup2<-vector(length=75)

for(i in 1:75){
reg2=dynlm(ec_fr~ecinf_fr+gap_fr,start=c(1970,2+i),end=c(1979,4+i))
lambda1[i]=reg2$coef[2]
min1[i]=lambda1[i]+2*sqrt(vcov(reg2)[2,2])
sup1[i]=lambda1[i]-2*sqrt(vcov(reg2)[2,2])
lambda2[i]=reg2$coef[3]
min2[i]=lambda2[i]+2*sqrt(vcov(reg2)[3,3])
sup2[i]=lambda2[i]-2*sqrt(vcov(reg2)[3,3])}

plot(lambda1,ylim=c(-2,2.5))
lines(min1,col="red")
lines(sup1,col="blue")

plot(lambda2,ylim=c(-2,2.5))
lines(min2,col="red")
lines(sup2,col="blue")


#Allemagne
for(i in 1:75){
reg2=dynlm(ec_dm~ecinf_dm+gap_dm,start=c(1970,2+i),end=c(1979,4+i))
lambda1[i]=reg2$coef[2]
min1[i]=lambda1[i]+2*sqrt(vcov(reg2)[2,2])
sup1[i]=lambda1[i]-2*sqrt(vcov(reg2)[2,2])
lambda2[i]=reg2$coef[3]
min2[i]=lambda2[i]+2*sqrt(vcov(reg2)[3,3])
sup2[i]=lambda2[i]-2*sqrt(vcov(reg2)[3,3])}
plot(lambda1,ylim=c(-0.75,1.25))
lines(min1,col="red")
lines(sup1,col="blue")

plot(lambda2,ylim=c(-0.5,1.25))
lines(min2,col="red")
lines(sup2,col="blue")



plot(lambda1)
lines(min1,col="red")
lines(sup1,col="blue")
min1
sup1

plot(lambda2)
lines(min2,col="red")
lines(sup2,col="blue")

#pays-bas
for(i in 1:75){
reg2=dynlm(ec_nl~ecinf_nl+gap_nl,start=c(1970,2+i),end=c(1979,4+i))
lambda1[i]=reg2$coef[2]
min1[i]=lambda1[i]+2*sqrt(vcov(reg2)[2,2])
sup1[i]=lambda1[i]-2*sqrt(vcov(reg2)[2,2])
lambda2[i]=reg2$coef[3]
min2[i]=lambda2[i]+2*sqrt(vcov(reg2)[3,3])
sup2[i]=lambda2[i]-2*sqrt(vcov(reg2)[3,3])}
plot(lambda1)
lines(min1,col="red")
lines(sup1,col="blue")
plot(lambda2)
lines(min2,col="red")
lines(sup2,col="blue")

#espagne
for(i in 1:75){
reg2=dynlm(ec_es~ecinf_es+gap_es,start=c(1970,2+i),end=c(1979,4+i))
lambda1[i]=reg2$coef[2]
min1[i]=lambda1[i]+2*sqrt(vcov(reg2)[2,2])
sup1[i]=lambda1[i]-2*sqrt(vcov(reg2)[2,2])
lambda2[i]=reg2$coef[3]
min2[i]=lambda2[i]+2*sqrt(vcov(reg2)[3,3])
sup2[i]=lambda2[i]-2*sqrt(vcov(reg2)[3,3])}
plot(lambda1)
lines(min1,col="red")
lines(sup1,col="blue")
plot(lambda2)
lines(min2,col="red")
lines(sup2,col="blue")

#italie
for(i in 1:75){
reg2=dynlm(ec_it~ecinf_it+gap_it,start=c(1970,2+i),end=c(1979,4+i))
lambda1[i]=reg2$coef[2]
min1[i]=lambda1[i]+2*sqrt(vcov(reg2)[2,2])
sup1[i]=lambda1[i]-2*sqrt(vcov(reg2)[2,2])
lambda2[i]=reg2$coef[3]
min2[i]=lambda2[i]+2*sqrt(vcov(reg2)[3,3])
sup2[i]=lambda2[i]-2*sqrt(vcov(reg2)[3,3])}
plot(lambda1)
lines(min1,col="red")
lines(sup1,col="blue")
plot(lambda2)
lines(min2,col="red")
lines(sup2,col="blue")

#royaume-uni
for(i in 1:75){
reg2=dynlm(ec_uk~ecinf_uk+gap_uk,start=c(1970,2+i),end=c(1979,4+i))
lambda1[i]=reg2$coef[2]
min1[i]=lambda1[i]+2*sqrt(vcov(reg2)[2,2])
sup1[i]=lambda1[i]-2*sqrt(vcov(reg2)[2,2])
lambda2[i]=reg2$coef[3]
min2[i]=lambda2[i]+2*sqrt(vcov(reg2)[3,3])
sup2[i]=lambda2[i]-2*sqrt(vcov(reg2)[3,3])}
plot(lambda1)
lines(min1,col="red")
lines(sup1,col="blue")
plot(lambda2)
lines(min2,col="red")
lines(sup2,col="blue")