Assume the following linear relationship: $Y_i = \beta_0 + \beta_1 X_i + u_i$, where $Y_i$ is the dependent variable, $X_i$ a single independent variable and $u_i$ the error term.
According to Stock & Watson (Introduction to Econometrics; Chapter 4), the second least squares assumption is that data should be i.i.d.
- I understand what this means, and know that data should be representative from the population. But I do not understand HOW exactly the violation of this assumption makes the OLS estimators biased/inconsistent. Is it just via the non-serial correlation of errors assumption?