Econ 4310: Course Syllabus| last offered Spring 2023
Econ 7310 (formerly 706): Course Syllabus | last offered Spring 2023
Econ 8320 (formerly 722): Course Syllabus| last offered Spring 2023
Econ 705: Course Syllabus | last offered Fall 2018
Econ 104: Course Syllabus | last offered Spring 2014
(Note: all course materials are available through CANVAS)

Econ 722 | as offered in Spring 2019

Readings: HS stands for Herbst & Schorfheide, 2015; DS stands for Del Negro and Schorfheide (2011) “Macroeconometrics” in Oxford Handbook of Bayesian Econometrics, FVRRS stands for Fernandez-Villaverde, Rubio-Ramirez, and Schorfheide (2016) “Solution and Estimation Methods for DSGE Models” in Handbook of Macroeconomics

1) Introduction to Bayesian Inference: IntroBayes Slides, 705 Notes on Statistical Inference, HS Chapter 3, Robert (1994): The Bayesian Choice, Springer Verlag.
2) Importance Sampling: ImportanceSampling Slides, Importance Sampling Notes, HS Chapter 3.
3) Nonstandard Inference – Unit Roots: NonStandardInference Slides, Sims and Uhlig (1991, ECMA), Phillips (1987, ECMA), Stock (1991, JME), Journal of Applied Econometrics special issue on unit roots, 1991, Vol 6(4).
4) Nonstandard Inference – Set Identification: NonStandardInference SetID Slides, HS Chapter 3, Moon and Schorfheide (2012, ECMA).
5) Reduced-Form VARs: ReducedFormVAR Slides, DS, Del Negro and Schorfheide (2004, IER), Giannone, Lenza and Primiceri (2014, REStat), Sims and Zha (1998, IER)
6) Gibbs Sampling with VAR Applications: GibbsSampling Slides, DS, Geweke (1996, JoE), Tanner and Wong (1987, JASA)
7) Reduced-Form VAR Extensions: VARExtension Slides, References are provided in slides.
8) Structural VARs: StructuralVAR Slides, DS, Moon and Schorfheide (2018, QE), Kilian and Luetkepohl (2017, Structural Vector Autoregressive Analysis), Herbst and Caldera (Forthcoming, AEJ Macro) “Monetary Policy, Real Activity, and Credit Spreads: Evidence From Bayesian Proxy SVARS”
9) Introduction to DSGE Modeling: IntroDSGE Slides, HS Chapters 1 & 2, FVRRS Section 8.1
10) Confronting DSGE Models with Data: Model_v_Data Slides, FVRRS Sections 8.2 & 8.3 & 8.4, Methods vs Substance [Link]
11) Likelihood and Freq. Inference for DSGE Models: StatisticalInference Slides, HS Chapter 2, FVRRS Section 10
12) Bayesian Inference for DSGE Models: BayesianInference Slides, HS Chapter 4, FVRRS Sections 12.1 & 12.2
13) Sequential Monte Carlo Methods: SMC Slides, HS Chapter 5, FVRRS Section 12.3
14) Particle Filtering, PFMCMC, SMC2: ParticleFilter Slides, HS Chapters 7 & 8 & 9
15) Evaluation of DSGE Models: DSGEEvaluation Slides, FVRRS Section 12.4
16) Applications for DSGE Models: DSGEApplications Slides, references are listed in slides

Yale Econ 556a: Lecture 1, Lecture 2, Cowles Lunch Talk,MATLAB-SMC, MATLAB-PF, SMC Exercises, Particle Filtering Exercises, StudyQuestions | offered in Fall 2017

Stanford Guest Lecture: BayesianComputations | held in Fall 2017

Bradley Visitor Lectures (Rochester): Syllabus, Lecture 1, Lecture 2, Lecture 3, MATLAB Exercises (Lecture 1), MATLAB Exercises (Lecture 2), MATLAB Exercises (Lecture 3), StudyQuestions | held in Fall 2017

A Ph.D. Course at the Study Center Gerzensee | offered June 2019
Lecture 1: IntroBayes, ps1
Lecture 2: VARs, ps2, MATLAB2
Lecture 3: StateSpace, ps3, MATLAB3
Lecture 4: GibbsSampling, ps4, MATLAB4
Lecture 5: IntroDSGE, ps5, MATLAB5
Lecture 6: BayesianEstimationDSGE1, ps6, MATLAB6, MH Flow Chart
Lecture 7: BayesianEstimationDSGE2, ps7, MATLAB7
Lecture 8: SequentialMonteCarlo, ps8, MATLAB8
Lecture 9: ParticleFiltering, ps9, MATLAB9
Lecture 10: DSGEEvaluation