In this unit you will learn about how linear models in general, and linear models fitted by the OLS regression algorithm in particular, produce estimates.
To do so, you will synthesize several pieces from earlier units in the course. Namely, you will combine:
- Covariance and correlation from your work with random variables;
- The conditional expectation function and its best linear approximation, the BLP;
- Core estimation theory about bias, consistency, and efficiency;
- Convergence in distribution from the Central Limit Theorem; and,
- Plug-in estimators from samples as population estimators.
Let's get to work!