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.

The course is aimed at students interested in working in academic or research positions, with the potential of publishing in refereed scientific journals. Students should gain in-depth knowledge about empirical time series analysis, achieve a good foundational understanding of Bayesian (time series) econometrics, and be able to apply their knowledge independently for their own research papers, or theses.

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

Course materials will be made available to participants in the form of slides and computer code. The slides are partly based on the following books:

• Chan, J., Koop, G., Poirier, D.J. and Tobias, J.L.: "Bayesian Econometric Methods" (Cambridge University Press)
• Hamilton, J.D.: "Time Series Analysis" (Princeton University Press).
• Kim, J.C. & Nelson, C.R.: "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" (MIT Press).

The lecture consists of two main blocks. First, we will discuss the topics listed in this syllabus based on the course materials (slides and codes) mentioned above. Second, you are asked to conduct your own research projects in groups. The groups (max. 5 students) are expected to hand in a paper and present their results during the lecture.

The relevant material for the exam is defined by what has been taught in the course.

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.

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