| 1 | Stationarity, lag operator, ARMA, and covariance structure | Lecture 1 Notes (PDF) |
| 2 | Limit theorems, OLS, and HAC | Lecture 2 Notes (PDF) |
| 3 | More HAC and intro to spectrum | Lecture 3 Notes (PDF) |
| 4 | Spectrum | Lecture 4 Notes (PDF) |
| 5 | Spectrum estimation and information criteria | Lecture 5 Notes (PDF) |
| 6 | GMM | Lecture 6 Notes (PDF) |
| 7–8 | Weak IV | Lecture 7 and 8 Notes (PDF) |
| 9 | Bootstrap | Lecture 9 Notes (PDF) |
| 10 | Introduction to VARs | Lecture 10 Notes (PDF) |
| 11 | VARs | Lecture 11 Notes (PDF) |
| 12–13 | Structural VARs | Lecture 12 and 13 Notes (PDF) |
| 14 | Factor models | Lecture 14 Notes (PDF) |
| 15 | Factor models part 2 | Lecture 15 Notes (PDF) |
| 16 | Empirical processes | Lecture 16 Notes (PDF) |
| 17 | Unit roots | Lecture 17 Notes (PDF) |
| 18 | More non-stationarity | Lecture 18 Notes (PDF) |
| 19 | Breaks and cointegration | Lecture 19 Notes (PDF) |
| 20 | Cointegration | Lecture 20 Notes (PDF) |
| 21 | Filtering, state space models, Kalman filter | Lecture 21 Notes (PDF) |
| 22 | State-space models, ML estimation, DSGE models | Lecture 22 Notes (PDF) |
| 23–24 | Intro to Bayes approach, reasons to be Bayesian | Lecture 23 and 24 Notes (PDF) |
| 25 | MCMC: Metropolis Hastings Algorithm | Lecture 25 Notes (PDF) |
| 26 | MCMC: Gibbs sampling | Lecture 26 Notes (PDF) |
