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X<-numeric(1000)
W<-rnorm(1000)
for(t in 2:1000)
X[t]<-1/3*W[t-1]+W[t]
plot(X, type='l')
acf(X)
A)Xt=3W(t-1)+Wt
X<-numeric(1000)
W<-rnorm(1000)
for(t in 2:1000)
X[t]<-3*W[t-1]+W[t]
plot(X, type='l')
acf(X)
B)Xt=2W(t-2)+0.5W(t-1)+Wt
X<-numeric(1000)
W<-rnorm(1000)
for(t in 3:1000)
X[t]<-2*W[t-2]+0.5*W[t-1]+W[t]
plot(X, type='l')
acf(X)
C) Xt = 0.2Xt−1 + 0.15Xt−2 + Wt con X1 = 2 con X2 = 4
X<-numeric(1000)
W<-rnorm(1000)
X1<-2
X2<-4
for (t in 3:1000)
X[t]<-.2*X[t-1]+0.15*X[t-2]+W[t]
plot(X, type='l')
acf(X1,X2)
D)Xt = 0.2Xt−1 + 0.24Xt−2 + Wt con X1 = 2 con X2 = 6
X<-numeric(1000)
W<-rnorm(1000)
X1<-2
X2<-6
for (t in 3:1000)
X[t]<-.2*X[t-1]+0.24*X[t-2]+W[t]
plot(X, type='l')
acf(X1,X2)
Calcula de manera anal´ıtica la funci´on de autocorrelaci´on de los procesos dados, para los primeros 5 periodos: ρ0, ρ1, ρ2, ρ3, ρ4. (CUADERNO)
Compara los valores estimados por R y los valores que calculaste analıticamente. Todas las gráficas y resultados son iguales.Algunos datos pueden interpretarse como 0 ya que en la gráfica se encuentran dentro del rango azul, independientemente de que en lo analítico es más específico.