This course deals with multivariate time series analysis for macroeconomic issues from a Bayesian point of view. After briefly reviewing univariate time series models, the course continues with multivariate vector autoregression models (VARs), using a Bayesian approach. For this, the course introduces the basics of Bayesian econometrics (including estimation, model selection, and prior choice). Afterwards, advanced aspects of VAR models are introduced, including the identification of structural shocks. Students apply and deepen knowledge of the material over the course of a small research project.

The course is aimed at students interested in working in academic positions and publishing their work in scientific journals. Students should gain in-depth knowledge about empirical 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; one missed unit is tolerated.

Course materials are presented in the form of slides, assignments are designed to use the R programming language. The last lecture is dedicated to the development of the research projects.

• Assignments (30%)
• Exam (40%)
• Projects (30%)
• Each part has to be positive

Grades

• 1: [88, 100]
• 2: [75, 88)
• 3: [62, 75)
• 4: [50, 62)
• 5: [0, 50)

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Students should have a sound knowledge of statistics (probability, random variables, expectations, joint/conditional distributions), mathematics (linear algebra, differential/integral calculus, algebra) and the foundations of econometrics (OLS / ML estimation), including familiarity with univariate time series econometrics (and should catch up otherwise).