Time Series Econometrics
Taught by: Chris Sims
New information is in red.
Course Syllabus and Reading List
Link to materials from last previous version of the course.
The course meets 9-10:30 Monday and Wednesday
in Fisher B06.
Exercises, links to additional notes,
announcements about the course, etc. will appear here.
- Takehome final exam
- Data for first exam question as .RData or text file.
- Notes: Bayesian Basics
- Notes: Bayesian and Frequentist Asymptotics
- Notes: Dynamic Factor Models
- Notes: Hidden Markov Chains
- Notes: The Minnesota Prior
- Notes: Structural VAR's
- Notes: trends in VAR models
- Notes: cointegration
- Exercise due 9/29
- Notes on the Kalman filter
- Notes on Markov Chain Monte Carlo
- Notes on Conditional Expectation, Stochastic Process Definition
- Exercise due Wednesday, October 13
- You can use any Kalman filter code, or write your own. A program (csminwel) to do unconstrained optimization for matlab or R is available at
Kalman filtering and smoothing programs are available for R and Matlab.
- R code for the simple Metropolis class example
- Notes on ARMA systems
- Notes on Granger Causal Priority
- Notes on system likelihood
- Notes on error bands for impulse responses, testing GCP
- Midterm exam with answers
- MCMC Model comparison