The course focuses on econometric methods used with macroeconomic data. By the end, we aim at arriving close to the current research frontier. The lecture consists of the following main building blocks:

• Univariate and (high-dimensional) multivariate time series analysis;
• Bayesian econometrics, statistical computation and algorithms;
• Structural (identification of dynamic causal effects) and predictive inference;
• State-space models.

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 time series analysis, achieve a good foundational understanding of Bayesian econometrics, and be able to apply their knowledge independently for their own research papers, or thesis.

Attendance is mandatory for this course, two absences will be tolerated.

Course materials are self-contained and 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 course grade will be based on the following components:

• Exam (50 points);
• Quizzes (10 points);
• Assignments (40 points), in groups (max. 5 students).

To pass the course, a positive final exam score (50% or higher of total points) is required. The relevant material for the exam is defined by what has been taught in the course. The grading scheme is:

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

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michael.pfarrhofer@wu.ac.at