This course has six main components:

1. A brief review of the classical linear regression model.
2. Introduction to time series analysis: We discuss different concepts of stationarity, ARMA and ARIMA models.
3. Simultaneous equation models and vector autoregression models: These are the two main strategies to analyze a vector of endogenous variables.
4. More on vector autoregression models: Impuls reponse function, structural vector autoregression models and how to estimate vector autoregression models.
5. An introduction to panel data models: Introduction to fixed effects and random effects models. In this section, we also discuss causality.
6. Dynamic panel models and panel vector autoregression models.

The course is helpful for students interested in working at research institutions or financial institutions. Students should gain a deeper understanding of the most important tools used in applied (macroeconomic) time series analysis, their proper use and their limitations, illustrated by applications to questions considered in macroeconomics. There is a special focus on applying these methods in the statistical software R. Finally, the students should be enabled to conduct their own research projects in applied time series analysis based on their own R codes.

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

This lecture consists of two main blocks. In the first block, the lecturer presents the topics mentioned in the syllabus (slides, literature, and papers are provided). After each lecture, students get homework assignments to analyze problem sets with R and some previous exam questions. Students are encouraged to work in small groups but should hand in their homework assignments individually. The homeworks count for 50 points. Homeworks need to be sent in by the WU-Learn tool.

In the last lecture, there is an exam of around 120 minutes. The maximum number of points is 50. If the exam is held on campus, it will be closed book. If the exam was online then it would be open book. Like for the homeworks there will be an assignment on WU-learn.

Final exam (50 Points),

Homework assignments (50 Points).

35% threshold of total exam points is required for passing the course.

Grading Key: <60Points: fail; >61Points: sufficient; >71Points: satisfactory; >81points: good; >91Points: very good.

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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 basic econometrics (OLS/ML estimation).