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.

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

This lecture consists of two main blocks. In the first block, the lecture presents the topics mentioned in the syllabus (slides, literature, and papers will be provided). After each lecture, students get homework assignments to analyze problem sets with R. Students are encouraged to work in small groups but should hand in their homework assignments individually.

In the second block, students carry out a group research project (max. 4 students) by applying the discussed tools and models to a macroeconomic question of choice. It is also possible to reproduce an existing paper. The results of the project should be structured and written according to the guidelines of Economics Letters and should not exceed about 5 pages in length (excluding a separate title page with an abstract summarizing the paper; a complete list of references; a list of data sources). The paper should be explicit enough for a fellow student to be able to replicate all results (therefore, data sources must be documented, modelling choices should be defended, and the R code should be available). You should clearly explain what the research question is, why the question is interesting, and what you have learned. For the paper you need to conduct an estimation by yourself (the choice of software is R). Papers should be written in a professional format and will be marked down if they do not satisfy this criterion. The final paper that is due at the end of the semester (01. July 2020). The final paper has to be uploaded at the learn@wu assignment tool.

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

Final exam (65 Points), homework assignment (20 Points) and Research Paper (15 Points).

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

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