In classical OLS regression, I get that the assumptions:
- Strictly exogenous errors $\implies$ unbiased OLS estimator
- Errors are normally distributed $\implies$ allow to make tests (t-tests, etc.)
But what is the use of the i.i.d. random sample in the classical OLS regression? What can it allow us to do that before we couldn’t do?