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.
Details on qualifying degrees and grade requirements can be found at the department page.
Every applicant must complete a self-assessment. However, this assessment is not a test that you can fail: we want applicants to assess their knowledge of statistics, economics, and econometrics so that specific pre-courses can be taken, if necessary.
The self-assesment can be downloaded here.
Please click here to submit your self-assessment.
Applicants must submit an essay (Academic CV) on their academic background in view of the pursued master program on Econometrics. We strongly encourage you to use our PDF questionnaire.
Please click here for the Academic CV form.
Econometrics is an English-language Master’s program. Candidates must provide evidence of sufficient knowledge of English (at least level B2). We accept the following proofs:
Prepare the best for your studies in Econometrics with our IMPACT Program and benefit from a variety of academic and administrative support:
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, decision theory and probability theory which are required to analyze, apply and further develop statistical procedures.
The course
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.
You may use the following contact information:
We strongly encourage you to use our Academic CV form where you can fill in a questionnaire and download a PDF version. Please click here to download the form.
Please see the academic CV as an opportunity for you and us to summarize your motivation and suitability for the program. Feel free to include any information, such as prior courses, relevant skills, work experience, career goals etc., that you deem supportive of your application.
While we can offer the program free of tuition, we unfortunately cannot offer any scholarships for such costs. Some external agencies, possibly in your home country, may be able to assist.
There regularly are opportunities for paid positions as student research assistants, which helps cover parts of the cost of living.
There are several student dormitories for which Accommodation is arranged by the associated student union (Studierendenwerk). The international offices are also happy to help you to find your way around the private housing market. Further information can be found via the following links:
The winter term starts at October 1st. The lecture period usually begins in the 2nd week of the semester. Note that the lecture periods may vary between the UAR universities. Course dates and locations can be found in the online lecture catalogs:
It is often worthwhile to visit the website of the respective chair for further details on the schedule.
You should expect to spend a minimum of €750 to €900 per month for
Note that the above is a minimum calculation which can be easily exceeded, in particular because rents differ significantly between different cities and districts. Also consider extra costs that you face upon arrival in Germany, some of which are the deposit for accommodation, fees for residence permit, household items etc.
Good news is that students enjoy reductions on many occasions. Student tariffs are available for mobile contracts and many leisure activities.
Date and time | Title | Speaker | Location |
---|---|---|---|
June 5th 2024 (13:45 – 14:15) |
Moving Aggregate Modified Autoregressive Copula-Based Time Series Models (MAGMAR-Copulas) Without Markov Restriction | Sven Pappert (TU Dortmund) |
UDE |
June 5th 2024 (14:15 – 14:45) |
Inference in Regression Discontinuity Designs with High-Dimensional Covariates | Alexander Kreiß (Univ. Leipzig) |
UDE |
June 5th 2024 (16:00 – 16:30) |
Estimation of Realized VEC Models for Multivariate Volatility | Jan Vogler (RUB) |
UDE |
June 5th 2024 (16:30 – 17:00) |
Euro-MD and a New Macroeconomics Uncertainty Index for the Euro Area Countries | Pascal Goemans (FU Hagen) |
UDE |
June 5th 2024 (17:00 – 17:30) |
Co-Explosiveness of Corporate Credit Spreads | Marko Kerkemeier (FU Hagen) |
UDE |
Date and time | Title | Speaker | Location |
---|---|---|---|
August 23rd 2023 (10:30 – 11:00) |
Estimating causal effects using optimization-based methods | Karolina Gliszczynska (UDE) |
FU Hagen |
August 23rd 2023 (11:00 – 11:30) |
Macroeconomic survey forecasting in times of crises | Philip Letixerant (FU Hagen) |
FU Hagen |
August 23rd 2023 (11:30 – 12:00) |
Forecasting Macroeconomic Tail Risk in Real Time: Do Textual Data Add Value? | Jan Prüser (TU Dortmund) |
FU Hagen |
August 23rd 2023 (13:30 – 14:00) |
Dynamics of government spending anticipation | Pascal Goemans (FU Hagen) |
FU Hagen |
August 23rd 2023 (14:00 – 14:30) |
Regime-specific exchange rate predictability | Marco Kerkemeier (FU Hagen) |
FU Hagen |
August 23rd 2023 (14:30 – 15:00) |
Factor-Based IVX Predictive Regression | Fabian Schmidt (TU Dortmund) |
FU Hagen |
August 23rd 2023 (15:45 – 16:15) |
The Multidimensional Nature of Political Instability and Economic Growth - A Text Mining Approach | Niklas Benner (TU Dortmund / RGS Econ) |
FU Hagen |
August 23rd 2023 (16:15 – 16:45) |
Marginal expected shortfall regressions | Yannick Hoga (UDE) |
FU Hagen |
Date and time | Title | Speaker | Location |
---|---|---|---|
February 7th 2023 (14:00 – 14:25) |
Different Narratives: The Effects of Fiscal Policy in Non-Gaussian SVARs with a Novel Prior for Instrumental Variables | Sascha Keweloh (TUDO) |
TU Dortmund University |
February 7th 2023 (14:30 – 14:55) |
Structural Periodic Vector Autoregressive Analysis | Daniel Dzikowski (TODU) |
TU Dortmund University |
February 7th 2023 (15:00 – 15:25) |
Median-based splitting rules for the causal tree | Lennard Maßmann (UDE) |
TU Dortmund University |
February 7th 2023 (16:30 – 16:55) |
An information-enriched adaptive Lasso ADF test | Martin Arnold (UDE) |
TU Dortmund University |
February 7th 2023 (17:00 – 17:25) |
Detecting the Predictive Power of Imperfect Predictors with Slowly Varying Components | Matei Demetrescu (TUDO) |
TU Dortmund University |
February 7th 2023 (17:30 – 18:00) |
A new test for portfolio weights | Vasyl Golosnoy (RUB) |
TU Dortmund University |
Date and time | Title | Speaker | Location |
---|---|---|---|
June 27th 2022 (13:55 – 14:25) |
Backtesting Systemic Risk Forecasts Using Multi-Objective Elicitability | Yannick Hoga (UDE) |
Ruhr-University Bochum |
June 27th 2022 (14:25 – 14:55) |
CRPS Learning | Florian Ziel (UDE) |
Ruhr-University Bochum |
June 27th 2022 (14:55 – 15:25) |
Estimating heterogeneous treatment effects with Bayesian Additive Regression Trees (BART) | Lennard Maßmann (UDE) |
Ruhr-University Bochum |
June 27th 2022 (16:00 – 16:30) |
Unrestricted maximum likelihood estimation of multivariate realized volatility models | Jan Vogler (RUB) |
Ruhr-University Bochum |
June 27th 2022 (16:30 – 17:00) |
A combined shrinkage and pooling prior for VARs | Jan Prüser (TUDO) |
Ruhr-University Bochum |
June 27th 2022 (17:00 – 17:30) |
Monitoring the Predictability of Stock Returns - The Impact of Unknown Predictor Persistence and Nonstationary Volatility | Fabian Schmidt (TUDO) |
Ruhr-University Bochum |
Date and time | Title | Speaker | Location |
---|---|---|---|
January 14th 2022 (9:45 – 10:15) |
Cheater Analysis | Natalie Reckmann (UDE) |
Zoom – hosted by UDE |
January 14th 2022 (10:15 – 10:45) |
Bayesian analysis of reduced rank regression models using post-processing | Markus Pape (RUB) |
Zoom – hosted by UDE |
January 14th 2022 (10:45 – 11:15) |
Modeling Realized Covariance Measures with Heterogeneous Liquidity: A Generalized Matrix-Variate Wishart State-Space Model | Bastian Gribisch (TU Do) |
Zoom – hosted by UDE |
January 14th 2022 (11:35 – 12:05) |
(Bootstrap) inference for doubly robust estimators | Tanvir Hossain (TU Do) |
Zoom – hosted by UDE |
January 14th 2022 (12:05 – 12:35) |
On the Incidental Parameter Problem in Fractional Response Models with Fixed Effects | Amrei Stammann (RUB) |
Zoom – hosted by UDE |
January 14th 2022 (13:45 – 14:15) |
Modeling and Forecasting Gas Prices with Copula Models | Sven Rappert (TU Do) |
Zoom – hosted by UDE |
January 14th 2022 (14:15 – 14:45) |
Predictive power of the variance premium | Yuze Liu (FUH) |
Zoom – hosted by UDE |
January 14th 2022 (14:45 – 15:15) |
New stylized facts of financial exuberance | Marco Kerkemeier (FUH) |
Zoom – hosted by UDE |
Date and time | Title | Speaker | Location |
---|---|---|---|
July 21st 2021 (10:20 – 12:20) |
Robust Splitting Methods for the Causal Tree | Karolina Gliszczynska (UDE) |
Zoom – hosted by FUH |
July 21st 2021 (10:20 – 12:20) |
Accurate and (Almost) Tuning Parameter Free Inference in Cointegrating Regressions | Karsten Reichold (AAU Klagenfurt and TU Do) |
Zoom – hosted by FUH |
July 21st 2021 (10:20 – 12:20) |
Higher-order moments in structural VAR models | Sascha Keweloh (TU Do) |
Zoom – hosted by FUH |
July 21st 2021 (13:30 – 14:45) |
Improving financial volatility nowcasts | Yuze Liu (FUH) |
Zoom – hosted by FUH |
July 21st 2021 (13:30 – 14:45) |
Empirical Similarity in Portfolio Selection | Jamol Bahromow (RUB) |
Zoom – hosted by FUH |
July 21st 2021 (14:45 – 16:00) |
Approximation and Error Analysis of Forward-Backward SDEs driven by Pure Jump Lévy Processes using Shot Noise Series Representations | Till Massing (UDE) |
Zoom – hosted by FUH |
July 21st 2021 (14:45 – 16:00) |
Valid weighted bootstrap inference requires consistent bias-correction | Christopher Walsh (TU Do) |
Zoom – hosted by FUH |
Date and time | Title | Speaker | Location |
---|---|---|---|
December 10th 2020 (10:30 – 10:55) |
Nonparametric Cointegrating Regression | Fabian Knorre (TU Do) |
Zoom – hosted by TU Do |
December 10th 2020 (11:05 – 11:30) |
A global-local prior for time-varying parameter VARs and Monetary Policy | Jan Prüser (TU Do) |
Zoom – hosted by TU Do |
December 10th 2020 (13:30 – 14:05) |
Forecasting with Deep Factor Models | Simon Umbach (FUH) |
Zoom – hosted by TU Do |
December 10th 2020 (14:05 – 14:30) |
A mixed frequency stochastic volatility model with two macro-financial components | Yuze Liu (FUH) |
Zoom – hosted by TU Do |
December 10th 2020 (15:10 – 15:35) |
Estimation of unrestricted CAW models in large samples | Jan Vogler (RUB) |
Zoom – hosted by TU Do |
December 10th 2020 (15:45 – 16:20) |
Adaptive Testing for Long Memory | Thilo Reinschlüssel (UDE) |
Zoom – hosted by TU Do |