LEC # | TOPICS | FILES |
---|---|---|

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) |

## Lecture Notes

## Course Info

##### Learning Resource Types

*notes*Lecture Notes

*assignment*Problem Sets

*Instructor Insights*