Syllabus
Registration via LPIS
Day | Date | Time | Room |
---|---|---|---|
Wednesday | 03/09/22 | 04:30 PM - 07:00 PM | D4.0.133 |
Wednesday | 03/23/22 | 04:30 PM - 07:00 PM | D4.0.133 |
Wednesday | 04/06/22 | 04:30 PM - 07:00 PM | Online-Einheit |
Wednesday | 04/27/22 | 04:30 PM - 07:00 PM | D4.0.133 |
Wednesday | 05/11/22 | 04:30 PM - 07:00 PM | D4.0.133 |
Wednesday | 05/25/22 | 04:30 PM - 07:00 PM | D4.0.133 |
Wednesday | 06/08/22 | 04:30 PM - 07:00 PM | D4.0.133 |
Tuesday | 06/14/22 | 06:00 PM - 08:30 PM | Online-Einheit |
Wednesday | 06/22/22 | 04:30 PM - 07:00 PM | D4.0.133 |
This course deals with uni- and multivariate time series analysis from an applied perspective. After briefly refreshing the knowledge about the linear regression model and methods to estimate the parameters of interest (ordinary least squares, maximum likelihood and general method of moments), we proceed with the analysis of univariate time series models (ARIMA). After discussing the problems of endogeneity, we continue with the analysis of vector autoregression models (VAR) and its extensions to incorporate structural characteristics (SVAR). We also analyze simultaneous equation models (SEM) which are another way to deal with endogeneity. Finally, we discuss the limits of time series analysis and the advantages of panel data.
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.
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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