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Consider a time series, generated using
set.seed(1)
E=rnorm(240)
X=rep(NA,240)
rho=0.8
X=0
for(t in 2:240){X=rho*X+E}
The idea is to assume that an autoregressive model can be considered, but we don't know the value of the parameter. ...

Another post about the R-squared coefficient, and about why, after some years teaching econometrics, I still hate when students ask questions about it. Usually, it starts with "I have a _____ R-squared... isn't it too low ?" Please, feel free to fi...

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)
> ...