The course gives an introduction to methods in Macroeconometrics with special emphasis on Bayesian techniques.
We will discuss the following topics:
- Vector Auto Regressions (VARs): estimation, identification, impulse response functions
- Dynamic Stochastic General Equilibrium (DSGE) models
- Likelihood methods: Kalman filter, ML estimation of DSGE models
- Introduction to Bayesian estimation and simulation
- Bayesian VARs (BVARs): priors for VARs, Bayesian estimation, structural BVARs
- Bayesian time series: Factor models, time-varying parameter models, Bayesian DSGE estimation and evaluation
- Model uncertainty and model misspecification
Form of evaluation:
- practical exercises
- short research paper
Upon completion of the course, students will be familiar with methods employed in macroeconometrics. Students will have the opportunity to apply macroeconometric methods in practical exercises. Students will also submit a short research paper using macroeconometric methods. The research paper can be based on their own research project or outline a novel research question.
Combination of lectures and practical exercises. Students can also give a short presentation of their research proposal.
The course grade will be determined by a combination of the practical exercises and the submitted research paper or proposal.
Students are presumed to have knowledge of econometrics at the intermediate undergraduate level. No prior knowledge of Bayesian estimation is required. Some basic knowledge of programming using Matlab or R is expected for the practical exercises.