1989 Advanced Macroeconometrics: Applications
Prof. Dr. Gernot Doppelhofer, Ph.D., PD Florian Huber, Ph.D.
Contact details
Weekly hours
Language of instruction
09/16/16 to 09/25/16
Registration via LPIS
Notes to the course
Day Date Time Room
Tuesday 12/06/16 01:00 PM - 05:00 PM D4.2.008
Wednesday 12/07/16 09:00 AM - 01:00 PM D4.2.008
Monday 12/12/16 11:00 AM - 02:00 PM D4.2.008
Tuesday 12/13/16 09:00 AM - 01:00 PM D4.2.008
Wednesday 12/14/16 09:00 AM - 01:00 PM D4.2.008
Thursday 12/15/16 09:00 AM - 01:00 PM D4.2.008

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

Learning outcomes
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.
Teaching/learning method(s)
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.
Prerequisites for participation and waiting lists

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.

Last edited: 2016-04-28