Monday, June 5

Introduction to Bayesian Inference

i. Motivation: DSGE models and their applications
ii. Bayesian analysis of linear regression models
iii. Markov Chain Monte Carlo (MCMC) methods

Day I lecture notes
Bayes linearreg.pdf
Policy motivation.pdf
Simulation based inference.pdf

Tuesday, June 6

Bayesian Estimation of DSGE Models
i. A prototypical New Keynesian DSGE model
ii. Constructing log-linear approximations
iii. The likelihood function
iv. Challenges for DSGE model estimation
v. Implementation of Bayesian analysis: posterior distributions of DSGE model parameters

Day 2 lecture notes
Challenges.pdf
Implementation.pdf
Likelihood dsge.pdf
Prototypical dsge.pdf
Solving dsge.pdf

Wednesday, June 7

Bayesian Estimation of DSGE Models (continued)
i. Some extensions: models with regime shifts, models with indeterminacies

Evaluation Methods

i. Posterior odds comparisons
ii. Prior and posterior predictive checks
iii. Applications

Day 3 lecture notes
Application.pdf
Indeterminacy.pdf
Posterior odds.pdf
Predictive checks.pdf
Regime switches.pdf

Thursday, June 8

Vector Autoregressions
i.Theoretical properties of VARs and likelihood function
ii. Bayesian analysis of VARs
iii. Identification of VARs
iv. Applications
v. VARs and DSGE models

Day 4 lecture notes
Var and dsge.pdf
Var beyesian analysis.pdf
Var properties and likelihood.pdf
Var structual.pdf

Friday, June 9

Combining Vector Autoregressions and DSGE Models
i. Constructing priors from DSGE models for VARs
ii. Using DSGE-VARs for forecasting
iii. Model evaluation with DSGE-VARs
iv. Policy analysis with DSGE-VARs

Day 5 lecture notes
Dsge var.pdf

References

An and Schorfheide 2006.pdf