Bayesian Econometrics
Department of Economics, University of Pretoria,
South Africa, December 5th to 9th 2011
Detailed description of lectures and lab sessions
Lecture notes
- Lecture 1: Introduction to Bayesian thinking
- Lecture 2: Monte Carlo methods
- Lecture 3: Hierarchical models
- Lecture 4: Dynamic Models
- Lecture 5, part I: Sequential Monte Carlo methods for state learning (pure filter)
- Lecture 5, part II: Sequential Monte Carlo methods for state and parameter learning
R code
- Lab session 1: Bayesian linear regression (logwages-yearseducation.txt)
- Lab session 2: Student’s t regression
- Lab session 2: Logistic regression
- Lab session 2: Heteroskedastic regression
- Lab session 3: First order dynamic linear model
- Lab session 3: Nonlinear dynamic model
- Lab session 3: Stochastic volatility model
- Lab session 4: AR(1) plus noise – pure filter
- Lab session 4: AR(1) plus noise – pure filter (subroutines)
- Lab session 4: AR(1) plus noise – parameter learning
- Lab session 4: AR(1) plus noise – parameter learning (subroutines)
More R code
- Piecewise linear regression (SA household health & education expenditures) (Results)
- DLM (SA stock market index (prices)) (Results)
- SV-AR(1) model (SA stock market index (returns)) (Results 1) (Results 2) (Results 3)
Coded in the lab sessions
- Bayesian linear regression via Gibbs sampler
- Kalman filter and kalman smoother (SA CPI)
- Mixture of two normals
- AR(1) plus noise via BF and APF
Other useful links