One of my homework problems is a simulation that compares three estimators (least squares, ridge regression with known parameters, and ridge regression with estimated parameters) for the following model $$Y_i = \beta X_i + \epsilon_i,\quad \epsilon_i\sim N(0,\sigma^2)$$
I am supposed to do 1000 replications with $X_i\sim N(0,2)$. Initially I generated my $X$ vector of data and used the same $X$ vector for each of the 1000 repetitions (so only thing different between repetitions is what random error gets added on).
Then I thought that might be wrong and that I should generate new $X$ data between each repetition.
What is the correct thing to do?
I can provide code if need be, but not really necessary to answer my question.