Exercises
Problem sets for the course, as PDFs. Each set is meant to be worked after the corresponding block of lectures; justify every answer. Solutions are distributed separately.
1 List 1 — Foundations
The data-generating process, the conditional expectation and the linear projection, and the algebra and geometry of least squares.
2 List 2 — Finite and Large Samples
Unbiasedness and omitted-variable bias, the sandwich variance and Gauss–Markov, the exact and asymptotic distributions of OLS, consistency, and a shrinkage estimator.