Teaching

General Overview

We offer a variety of courses from Statistics and Econometrics for Bachelor, Master and Doctoral students in Economics, Mathematical Finance and Social and Economic Data Analysis.

Bachelor students start out with introductory courses on Statistics (Statistics I & II) and Econometrics (Econometrics I, typically offered by Prof. W. Pohlmeier), get more practice in empirical modeling in Applied Econometrics before writing their Bachelor thesis.

On the Master’s level (Master of Economics, Master of Mathematical Finance, Master of Social and Economic Data Analysis) we offer courses that focus on different aspects in the econometric analysis of time series data (Applied Time Series Analysis, Advanced Time Series Analysis). In addition, we offer econometric seminars where students prepare their own empirical project. Students specialized in statistics and econometrics may write their Master’s thesis at the Chair of Statistics and Econometrics.

The course on Advanced Time Series Analysis is a mandatory course for students of the Doctoral Program in Economics and Finance.

Courses for Bachelor Students:

Statistics I
Statistics II
Applied Econometrics
Seminar Applied Econometrics

Courses for Master Students:

Applied Time Series Analysis
Advanced Time Series Analysis
Seminar Econometric Projects
Seminar Applied Econometric Projects

Courses for Doctoral Students:

Advanced Time Series Analysis

Seminar Selected Topics in Econometrics

Study plans

Sem. B.Sc.Econ. M.Sc.QE Doctoral Program QEF
1 - Advanced Econometrics Advanced Times Series Analysis
2 Statistics I Applied Time Series Analysis Microeconometrics
3 Statistics II

Advanced Time Series Analysis

Seminar:

Econometric Projects

 
4 Econometrics

Seminar:

Applied Econometric Projects

Master thesis

Seminar: Selected Topics in Econometrics
5

Applied Econometrics

Seminar: Applied Econometrics

   
6 Bachelor thesis    

More detailed information

Courses for Bachelor Students:

  • Statistics I: The lecture offers an introduction to statistical analysis. Topics covered include univariate and multivariate descriptive methods, explorative methods, probability, discrete and continuous random variables and their distribution. Tutorials complement the lecture and include a sessions discussing output from the statistical software STATA. This lecture is typically offered in summer terms. Lectures and tutorials are in English. More information can be found on the course homepage.
  • Statistics II: This lecture builds directly on Statistics I and gives an introduction to statistical estimation and inference. Topics covered include point and interval estimation, hypothesis testing and an introduction to regression analysis. Tutorials complement the lecture and include sessions discussing output from the statistical software STATA. This lecture is typically offered in winter terms. Lectures and tutorials are in English. More information can be found on the course homepage.
  • Applied Econometrics: The lecture covers additional econometric techniques that are useful in empirical economic research and is intended to acquaint students with important econometric concepts not covered in Econometrics, including panel data, causality, discrete choice as well as time series econometrics and forecasting. It is designed to train students in practical aspects of empirical economics. Lectures and tutorials are in English. Participants must have successfully completed “Econometrics” (or an equivalent course). This course is typically offered in winter terms. More information can be found on the course homepage.
  • Seminar Applied Econometrics: This seminar is based on the lecture called “Applied Econometrics” (see above). Students that take this course as a Bachelor seminar typically prepare an empirical study using real world data and present their results in class at the end of the semester. Lectures, tutorials and the seminar presentations are in English. Participants must have successfully completed “Econometrics” (or an equivalent course). This course is typically offered in winter terms. More information can be found on the course homepage.

Courses for Master Students:

  • Applied Time Series Analysis: Many economic variables are observed over time on a regular frequency (e.g. the quarterly growth rate of GDP, the monthly CPI inflation rate, daily interest rates, daily returns of the DAX stock market index). This type of data is known as time series data and often features correlation over time that can be exploited for forecasting. In this course econometric models for univariate time series data are introduced. Estimation and model specification as well as their use in forecasting is discussed. Students learn how to apply these methods in practice during computer sessions using Matlab. This course is typically offered in summer terms. More information can be found on the course homepage.
  • Advanced Time Series Analysis: Economic theory suggests that many macroeconomic time series (e.g. growth rates of gross domestic product, interest and inflation rates as well as monetary variables) are interrelated. In the lecture econometric methods for analyzing two or more time series (multiple time series) are introduced. Emphasis is given on the vector autoregressive framework, which is very popular in empirical macroeconomics and finance. It is used for forecasting and for investigating the dynamic interrelations between different variables (impulse response analysis). The course covers the analysis of stable vector autoregressive (VAR) models (including model estimation, model specification and model checking) as well as the statistical analysis of integrated and cointegrated variables. Further topics to be discussed include recent advances in structural VAR modeling, panel unit roots and cointegration, factor augmented VARs, time-varying parameter and Bayesian VARs. If time permits, multivariate GARCH models will be covered. Students gain practical experience in VAR modeling during the computer sessions. This course is typically offered in winter terms. More information can be found on the course homepage.
  • Seminar Econometric Projects: In this seminar participants prepare an empirical study using real world data and econometric techniques and software. Students should demonstrate their ability to use econometric techniques to analyze specific economic problems. This typically includes reading and understanding the relevant literature, collecting data, specifying and analyzing an econometric model and discussing the empirical results. Participants must have successfully completed “Econometrics” (or an equivalent course) or “Advanced Econometrics” and one additional course helpful for time series analysis (e.g. Advanced Time Series Analysis, Applied (Univariate) Time Series Analysis, Financial Econometrics or Zeitreihenanalyse (Prof. Beran)). Some knowledge in using econometric software and programming Matlab is recommended. This seminar is typically offered in winter terms.
  • Seminar Applied Econometric Projects: In this seminar participants prepare an empirical study using real world data and econometric techniques and software. Students should demonstrate their ability to use econometric techniques to analyze specific economic problems. This typically includes reading and understanding the relevant literature, collecting data, specifying and analyzing an econometric model and discussing the empirical results. Participants must have successfully completed “Econometrics” (or an equivalent course) or “Advanced Econometrics” and one additional course helpful for time series analysis (e.g. Advanced Time Series Analysis, Applied (Univariate) Time Series Analysis, Financial Econometrics or Zeitreihenanalyse (Prof. Beran)). Without this background participation is not possible. Some knowledge in using econometric software and programming Matlab is recommended. This seminar is typically offered in summer terms. More information can be found on the seminar homepage.

Courses for Doctoral Students:

  • Advanced Time Series Analysis: Economic theory suggests that many macroeconomic time series (e.g. growth rates of gross domestic product, interest and inflation rates as well as monetary variables) are interrelated. In the lecture econometric methods for analyzing two or more time series (multiple time series) are introduced. Emphasis is given on the vector autoregressive framework, which is very popular in empirical macroeconomics and finance. It is used for forecasting and for investigating the dynamic interrelations between different variables (impulse response analysis). The course covers the analysis of stable vector autoregressive (VAR) models (including model estimation, model specification and model checking) as well as the statistical analysis of integrated and cointegrated variables. Further topics to be discussed include recent advances in structural VAR modeling, panel unit roots and cointegration, factor augmented VARs, time-varying parameter and Bayesian VARs. If time permits, multivariate GARCH models will be covered. Students gain practical experience in VAR modeling during the computer sessions. This course is typically offered in winter terms. More information on course homepage.

Seminar Selected Topics in Econometrics: Doctoral students specialized in empirical research