Let’s consider the usual linear regression model, with the full set of assumptions: y = Xβ + ε ; ε ~ N , (1)where X is a non-random (n × k) mat...

Read more »

Consider a (stationary) autoregressive process, say of order 2, for some white noise with variance . Here is a code to generate such a process, > phi1=.5 > phi2=-.4 > sigma=1.5 > set.seed(1) > n=240 > WN=rnorm(n,sd=sigma) > ...