Time Series Econometrics
Economics 513
Fall 2010

Taught by: Chris Sims

Contact information

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 http://sims.princeton.edu/yftp/optimize/ 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