Time Series Analysis

Lecture Notes

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)