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

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... [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) __ ... [Read more...]