In the MAT8181 graduate course on Time Series, we started discussing autoregressive models. Just to illustrate, here is some code to plot – causal – process, > graphar1=function(phi){ + nf <- layout(matrix(c(1,1,1,1,2,3,4,5), 2, 4, byrow=TRUE), respect=TRUE) + e=rnorm(n) + X=rep(0,n) + for(t in 2:n) X=phi*X+e + plot(X,type="l",ylab="") + abline(h=mean(X),lwd=2,col="red") + abline(h=mean(X)+2*sd(X),lty=2,col="red") + abline(h=mean(X)-2*sd(X),lty=2,col="red") + u=seq(-1,1,by=.001) + plot(0:1,0:1,col="white",xlab="",ylab="",axes=FALSE,ylim=c(-2,2),xlim=c(-2.5,2.5)) + polygon(c(u,rev(u)),c(sqrt(1-u^2),rev(-sqrt(1-u^2))),col="light yellow")...