Program Structure

ME8: Master Thesis

The master thesis demonstrates that students are able to independently apply and adapt scientific methods to an econometric problem within a given period of time. The processing time is six months. Topics for final theses are offered each semester by several university lecturers, so that students can choose between different offers. Students can also make their own suggestions for topics.

ME4: Time Series Analysis

The course initially covers methods of descriptive time series analysis. Then, structural theory and estimation of time series models are discussed. Core topics include approximation and elimination of trends, the theory of linear filters, ‘naive’ forecasting, exponential smoothing, stationary stochastic processes, optimal linear forecasts, ARMA-processes, the autocorrelation function, model identification and parameter estimation in the time domain.

ME6: Applied Econometrics

ME6 includes courses that focus on the application of advanced econometric methods to selected economic problems whereby emphasis is typically set on acquisition, processing and analysis of real data sets. In many courses, participants acquire in-depth knowledge in statistical programming.

• Econometric Analysis Electricity and Financial Markets
• Applied Time Series Analysis in Macroeconometrics
• Topics in Economic Policy Evaluation
• Microeconometrics in Health Economics
• Econometric Applications in Monetary Economics

Electives

Participants acquire knowledge about current theoretical developments in micro- or macroeconomics, applied econometrics and econometric methods by attending selected courses from blocks ME5: Economics, ME6: Applied Econometrics and ME7: Econometric Methods. Focus lies on the discussion, adaptation and application of various econometric tools on the one hand and on advanced and up-to-date topics of economic interest on the other hand. .

ME5: Economics

Many econometric methods have been developed in line with developments in economics. Therefore, it is important for our students to be familiar with more recent theoretical developments and thus potential areas of application of econometric methods. Depending on the field of interest, the courses have a micro- or macroeconomic orientation. The curriculum covers a variety of courses from the following disciplines:

• Applied Macro- and Microeconomics
• Dynamic Macroeconomics
• Game Theory
• Health Economics
• Public Economics
• Financial Economics

ME7: Econometric Methods

Modules in ME7 have a strong methodological orientation, i.e. students have the opportunity to acquire in-depth knowledge of econometric methods in selected fields, for example:

• Stochastic Processes
• Unit Root and Cointegration Analysis
• Analysis of Big Data and Machine Learning
• Bayesian Econometrics
• Multivariate Statistical Methods
• Advanced Microeconometrics

ME1: Statistical Theory

Participants learn to use the formal language of statistics and gain knowledge of fundamental concepts in stochastic, decision theory and asymptotic theory which are required in order to analyze, apply and further develop statistical procedures.

ME1 consists of the following courses:

_ Probability Theory (ME1a)

An introduction to measure theory and concepts in stochastic which are necessary to formalize the questions discussed in statistical theory.

_ Decision Theory (ME1b)

Introduces the basic concepts associated with statistical tests.

_ Probability Theory (ME1c)

Deals with asymptotic properties of statistical methods and presents various central limit theorems used in statistics.

ME2: Econometrics

ME2 deals with a wide range of fundamental econometric methods. Special emphasis is placed on asymptotic results to allow for a general discussion of the statistical properties of these methods. The main focus lies on a formally precise description of the concepts. Topics include the linear regression model, the generalized linear regression model, maximum likelihood estimation and inference, asymptotic theory, endogenous regressors, instrumental variables, generalized method of moments and regression models for time series data, among others.

ME3: Case Studies

The participants solve statistical problems in larger group projects, usually using raw economic data. They are trained in applied research and acquire skills in presenting statistical results and various interdisciplinary qualifications such as teamwork and know-how in project management, communication and consulting. Furthermore, students expand their methodological knowledge and gather experience in statistical programming.

Overview

The four-semester master’s program in econometrics is divided into compulsory modules, elective courses and closes with the master thesis. In four compulsory modules (ME1 - ME4) with a total amount of 43 credit points, you acquire a sound knowledge in statistics, econometrics and time series analysis. The core curriculum also conveys skills in applied research, statistical programming, research project management and statistical consulting.

Elective modules (ME5 - ME6) train students in economic theory (ME5) provide further specialization in applied (ME6) and theoretical econometrics (ME7). In ME6 and ME7, students focus on a specific field of research according to their individual preferences. This results in a flexible number of credits to be acquired by electives, which must, however, amount to a minimum of 11 and a maximum of 26 credit points in each of the elective module areas.

While core modules and many electives are taught in English, students have the opportunity to attend selected German-language courses.

Further information on the individual course areas can be viewed by clicking in the interactive semester plan above. Please note that the above plan is for orientation only. Students are free to decide in which order they complete core courses and elective modules.

Details of all courses offered can be found in the following table. Please note that this list will be updated regularly.