Syllabus
I. Course description:
The lecture covers advanced and computational intensive estimation and inference techniques with an emphasis on hands-on exercises using the econometric software Matlab.
- Introduction to Bayesian statistics
- Bayesian estimation of the linear regression model: closed form solutions
- Bayesian estimation of the linear regression model: numerical solutions
- Bayesian estimation of the nonlinear regression model: the Metropolis-Hastings algorithm
- Bayesian estimation of VAR models with natural conjugate prior
- Bayesian estimation of VAR models with DSGE prior (if time allows)
II. Prerequisities:
The course assumes knowledge of the topics taught in Advanced Statistics, Econometrics I and Econometrics II.
III. Exam:
- 5 LP
- written exam (Date: see UniVis)
IV. Literature:
Main textbooks (for details see the handouts for each lecture):
- Koop, G. (2003), Bayesian Econometrics, Wiley.
- Koop, G., D.J. Poirier, J.L. Tobias (2007) Bayesian Econometric Methods, Cambridge University Press.