Topics in Empirical Macroeconomics and Finance is a PhD-level course focused on modern empirical methods in macroeconomics with a strong emphasis on Bayesian approaches. Topics include Bayesian foundations and VARs, identification strategies like SVARs and local projections, advanced time series models such as state-space and time-varying parameter models, and applications of machine learning in macroeconomic forecasting. The course combines theoretical instruction with hands-on coding labs and student-led discussions, culminating in a research presentation.

By the end of the course, students will be able to:

• Apply Bayesian methods to empirical macroeconomic models.
• Evaluate and implement identification strategies in macroeconomic research.
• Critically assess contemporary empirical macroeconomic literature.
• Conduct and present original or replication-based empirical research using public data.

Attendance is mandatory.

The course consists of three parts. The first part are lectures by Prof. Doppelhofer and Prof. Huber which serve to introduce the students to key topics in Bayesian macroeconometrics. The second part are coding labs (in R) which illustrate the implementation and usage of the methods discussed in the lectures. The final part is a seminar (to be held on the final two days of the intensive course) where students present their own research.

Grading will be based on active participation (1/3),  a paper presentation (1/3) and a short exercise sheet (1/3).

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