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