The course focuses on econometric methods used in applications to aggregate macroeconomic data and considers time series analysis from a Bayesian perspective. By the end, we aim at arriving close to the current research frontier. Topics are based on the following building blocks:

• Bayesian econometric methods, statistical computation and algorithms;
• (High-dimensional) univariate and multivariate time series analysis (including, e.g., vector autoregression and state-space models);
• Structural and predictive inference (e.g., identification of macroeconomic shocks or forecast evaluation schemes);
• Advanced topics (e.g., machine learning-inspired approaches).

Besides introducing students to such state-of-the-art techniques, an additional focus is to provide them with the necessary knowledge in statistical software (we will use R) to conduct their own research projects. Students will be provided with theoretical inputs alongside empirical examples.

Attendance is mandatory for this course, one absence will be tolerated.

The course grade will be based on the following components:

• Final exam (40 points)
• Exercises (20 points)
• Research project (40 points)

To pass the course, a positive final exam score and a positive score on the research project (50% or higher of respective total points) are required. The grading scheme is:

• Very good (1): [89, 100] points
• Good (2): [78, 89) points
• Satisfactory (3): [60, 78) points
• Sufficient (4): [50, 60) points
• Fail (5): [0, 50) points

The final exam will consist of a mix of multiple-choice and open questions, and will last for 60 minutes. It is scheduled to take place in our penultimate lecture.