The program is aimed at quantitatively oriented students with a strong interest to acquire the necessary theoretical foundations for a career in econometric research. Especially the core modules are demanding in terms of mathematical rigor!
A BSc or equivalent degree in Economics, Mathematics, Statistics or a related discipline is required. At least 15 ECTS in both Economics and mathematics/statistics/econometrics are required.
Candidates who do not fully meet these credit requirements may in general be admitted on the condition that they successfully complete certain undergraduate level courses. If admission is subject to conditions, applicants will be informed thereof in their notification of admission. In any case, a regular application must first be submitted in due time.
Applicants must submit an essay of at most two pages on their academic background in view of the pursued master program on Econometrics.
Econometrics is an English-language Master’s program. Candidates must provide evidence of sufficient knowledge of English (at least level B2). We accept certificates for the following English-language tests: TOEFL internet-based test 100, International English Language Testing System (IELTS) Band 6.5 or similar certificates.
Christoph Hanck is Professor of Econometrics at University of Duisburg-Essen since August 2012. He received his doctorate in 2007, supervised by Prof. Dr. Walter Krämer, from TU Dortmund University. He subsequently joined the DFG Sonderforschungsbereich ‘Complexity Reduction in Multivariate Data Structures’ to then become a Postdoctoral Researcher at Maastricht University in 2008. From 2009 to 2012 he was Assistant and later Associate Professor in Statistics and Econometrics at Rijksuniversiteit Groningen. His research focuses on the analysis of nonstationary panel data and macroeconometrics. He is a faculty member of the Ruhr Graduate School in Economics.
Carsten Jentsch studied mathematics with a minor in business administration at TU Braunschweig from 2001 to 2007, where he received his doctorate in 2010. After a research stay at UC San Diego he became postdoc at the Economics faculty of the University of Mannheim in 2011 and at the SFB 884 ‘The Political Economy of Reforms’. Since 2015 he has been a member of the Elite Program for Postdocs of the Baden-Württemberg Foundation. After holding professorships at the Universities of Bayreuth and Mannheim, he has been working at TU Dortmund University since summer 2018. He is a faculty member of the Ruhr Graduate School in Economics.
Since 2002 Christoph M. Schmidt is head of the RWI - Leibniz Institute for Economic Research and professor at the Ruhr-Universität Bochum. He was member of the German Council of Economic Experts from 2009 to 2020 and was its Chairman from March 2013 to February 2020. Since 2019 he is member, and since 2020 co-chairman of the Franco-German Council of Economic Experts. Schmidt received his Ph.D. from Princeton University in 1991 and completed his habilitation in 1995 at the Ludwig-Maximilians-Universität (LMU) of Munich. From 1995 to 2002 Schmidt was a full professor for Econometrics at the Universität Heidelberg. Since 1992 he has been a Research Affiliate of the Centre for Economic Policy Research (CEPR), London, since 1996 a CEPR Research Fellow, and since 1998 he is also a Research Fellow at the Institute for the Study of Labor (IZA), Bonn.
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.
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 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.
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. .
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:
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:
Participants learn to use the formal language of statistics and gain knowledge of fundamental concepts in stochastics (M1a), decision theory (M1b) and asymptotic theory (M1c) which are required in order to analyze, apply and further develop statistical procedures.
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.
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.