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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?

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