Bayesian Econometric Analysis

“AAEC / ECON / STAT 6564”

Welcome to our course web site. New material will be added as we move along. Anything below the “In Progress” line is subject to change and edits.

Syllabus and General Announcements

  1. Syllabus (pdf)
  2. LaTeX material for syllabus
    • main tex file (tex)
    • bibliography (bib)
    • table with schedule called by the main script (tex)
    • excel file for table (xl)

Software Instructions

We will be using Matlab as our statistical programming package and LaTeX for word processing. The full Matlab package can be purchased by current VT students for $36 / year from the IT procurement and licensing solutions office at VT.

Installation should be straightforward - please see me if you run into any glitches.

As an alternative, you will have access to VT’s Advanced Research Computing (ARC) cluster system, and use Matlab through their onDemand interface. Instruction on how to do this are given here:

  1. Instructions (pdf)
  2. Instructions (tex)

Turning Matlab m.files (scripts, functions) into pdfs (and other formats)

  1. Instructions (pdf)
  2. Instructions (tex)

Matlab and Matrix Algebra Tutorial

Get a jump start at using Matlab, and a refresher on matrix algebra at the same time!

  1. Matlab Tutorial (my own version)(pdf)

  2. Matrix Algebra Tutorial (using Matlab, my own version) (PDF)

  3. Matlab code for Matrix Algebra Tutorial

Textbook Websites

Koop, Poirier & Tobias 2007

Course Content

Module 1: Introduction to Bayesian Inference

Bayesian vs. Classical Estimation / Bayesian Model Components

  1. Lecture Notes
  2. Matlab scripts

Conjugate analysis

  1. Univariate example
  2. Lecture Notes
  3. Matlab scripts

Common Bayesian Estimation “Tricks”

Module 2: Gibbs Sampling

  1. Lecture Notes
  2. Matlab scripts
  3. Matlab functions:
  4. data:

Module 3: Coverage and Prediction in Bayesian Analysis

  1. Lecture Notes
  2. Matlab scripts
  3. Matlab functions:

Module 4: Models with General Error Structure / Model Comparison

  1. Lecture Notes
  2. Matlab scripts
  3. functions:
  4. data:

Module 5: Data Augmentation / Latent Variable Models

  1. Lecture Notes

  2. Matlab scripts

  3. functions:

  4. data:

Module 6: Hierarchical Models

  1. Lecture Notes
  2. Matlab scripts
  3. functions:
  4. data:

Module 7: Metropolis-Hastings Algorithm

  1. Lecture Notes
  2. Lecture Notes AR1
  3. Matlab scripts
  4. functions
  5. log files
  6. data:

Module 8: Bayesian Model Search and Model Averaging

  1. Lecture Notes
  2. Matlab scripts
  3. functions
  4. log files
  5. data

Module 9: Selection, Treatment, and Switching Models

  1. Lecture Notes
  2. Matlab scripts
  3. log files
  4. functions

Module 10: Finite Mixture Models

  1. Lecture Notes
  2. Matlab Material
  3. functions:
  4. log files

Module 11: Re-parameterized Models: Ordered Probit & Hierarchical Ordered Probit

  1. Lecture Notes
  2. Matlab Material
  3. functions
  4. logs files
  5. data:

Module 12: Gibbs-within-Gibbs: Multinomial Probit Models

  1. Lecture Notes
  2. Matlab Material
  3. functions:
  4. logs:

Module 14: Importance sampling, Accept-reject sampling

  1. Lecture notes
  2. Matlab Material
  3. functions

Problem Sets

PS1 (due: Feb. 18)

  1. Instructions
  2. Matlab starter script
  3. Data (.raw format)

PS2 (due: Mar. 5)

  1. Instructions

PS3 (due: Mar. 26)

  1. Instructions
  2. Data (.txt format)

PS4 (due: Apr. 16)

  1. Instructions

PS5 (due: Apr. 28)

  1. Instructions
  2. Data (.txt format)

PS6 (due: May 8, 5pm)

  1. Instructions