Simuloi AR(1) mallia.
a<-0.5 x0<-0 T<-1001 seed<-2 set.seed(seed) # normaalijakauma e<-rnorm(T) x<-matrix(x0,T,1) for (t in 2:T) x[t]<-a*x[t-1]+e[t] plot(x,type="l") # t-jakauma df<-10 kh<-sqrt(df/(df-2)) e<-rt(T,df=df)/kh x<-matrix(x0,T,1) for (t in 2:T) x[t]<-a*x[t-1]+e[t] plot(x,type="l")
Simuloi MA(1) mallia.
# MA(1)-malli a1<-0.8 T<-1001 seed<-2 set.seed(seed) e<-rnorm(T) x<-matrix(0,T-1,1) for (t in 2:T) x[t]<-e[t]+a1*e[t-1] plot(x,type="l") cor<-a1/(1+a1^2) cor x0<-x[1:(length(x)-1)] x1<-x[2:length(x)] plot(x0,x1) aa<-seq(-3,3,0.01) bb<-aa/(1+aa^2) plot(aa,bb) mak<-max(bb) ind<-(bb==mak) mak aa[ind] #[1] 0.5 #[1] 1 # MA(2)-malli a1<--1 a2<--1 T<-1001 seed<-2 set.seed(seed) e<-rnorm(T) x<-matrix(0,T-2,1) for (t in 3:T) x[t]<-e[t]+a1*e[t-1]+a2*e[t-2] plot(x,type="l")