This course deals with multivariate time series analysis from an applied perspective. After briefly refreshing the knowledge about univariate time series models (ARMA) we will continue with the analysis of the multivariate case (VARs) and its extensions to incorporate structural characteristics (SVAR). After discussing the estimation routines in more depth, we will tackle the problem of identifying the nature of the structural shocks (short- vs. long-run restrictions and sign restrictions) to derive some recommendations for policymakers based on an impulse response analysis. Moreover, this course provides a brief introduction to the Bayesian paradigm in econometrics and its advantages compared to the frequentist approach.

The course will be helpful for students interested in working at research institutions or financial institutions. Rather than focus narrowly on the application of econometric tools in macroeconomics, we will try to convey a deeper understanding of the most important tools used in applied time series analysis, their proper use and their limitations, illustrated by applications to questions considered in macroeconomics. The discussed methods are used heavily in Central Banks and policy institutions and will be covered with a special emphasis on their applications and interpretations. Finally, the students will be enabled to conduct own small research projects applying time series analysis.

Attendance is mandatory (however, two missed units are tolerated)

This lecture consists of two main blocks. While in the first block we are discussing the topics mentioned in the syllabus (slides, literature, and papers will be provided), the second block is dedicated to students' presentations of famous examples in the VAR literature.  The group presentation (max. 5 students) is dedicated to a famous example in the VAR literature. It is expected that the group scrutinizes the paper in depth (objective, relevant assumptions, model framework, and results) and provide (a) a detailed discussion as well as (b) comments/questions/suggestions to the authors.

Please make sure that you read the assigned literature PRIOR to the lecture.

Final Exam (50 points), Paper presentation (20 points) and Exercises (30 points)

A positive final test (50% threshold of total exam points) is required for passing the course.

Grading Key: <60Points: fail; >60Points: sufficient; >70Points: satisfactory; >80points: good; >90Points: very good.

Students should have a sound knowledge of statistics (probability, random variables, expectations, joint/conditional distributions), mathematics (linear algebra, differential/integral calculus, algebra) and basic econometrics (OLS/ML estimation). moreover, it is expected that the students are familiar with univariate time series econometrics (if not, it is expected that students refresh their knowledge with "Applied Econometric Time Series" by Enders (CH1-4).)

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