# Time series applications in R

install.packages("dynlm")

require(dynlm)
library(dynlm)


gas<-read.table("~/Documents/Schoolwork-UIUC/Econ 508 Fall 2012 TA/R Session/gasq.data",header=TRUE)

summary(gas)

#define global variable
p<-gas$p
p=gas$p  
z<-gas$z
y<-gas$y

#classify variable as time series
p<-ts(p,start=1947, freq=4)
y<-ts(y,start=1947, freq=4)
z<-ts(z,start=1947, freq=4)

dym<-dynlm(d(p)~L(p,c(1,2)))  #L(p, 1:4)
summary(dym)

dym2<-dynlm(d(p)~d(L(p)),start=c(1950,1),end=c(2008,2))
summary(dym2)

lp<-lag(p,-1)
lp4<-lag(p,-4)
dp<-diff(p)

model<-dynlm(y~L(y,1)+d(L(y,1:2))+z+p+d(L(z,1:2))+d(L(p,1:2)))

#loops

beta0=0.2
beta1=0.3
alpha1=-0.5
alpha2=0.2

R=100
delta<-rep(0,R)
delta[1]=beta0
delta[2]=beta1+alpha1*beta0
for (i in 3:R){
	delta[i]=alpha1*delta[i-1]+alpha2*delta[i-2]
	}
	
Delta=cumsum(delta)

par(mfrow=c(1,2))
plot(1:R,Delta,type="l",main="cumulative effects")
abline(h=(beta0+beta1)/(1-alpha1-alpha2),col="red")
plot(1:R,delta,type="l",main="Impulse Response")