Master’s Theses

Information on Master’s Theses

  • You can earn a total of 30 ECTS for your Master’s thesis. In the case of the Master of Economics 5 of these ECTS are earned by presenting intermediate results of your thesis in our Statistics Seminar; the remaining 25 ECTS are earned by submitting the written thesis.
  • The grade is determined by the final written thesis. The intermediate report is a non-graded program achievement.
  • In the intermediate report you should demonstrate that you familiarized yourself with your chosen topic. That includes an overview of the relevant literature and first empirical results. At this point, your working progress should indicate positive prospects for your thesis project. The presentation is also meant to show that you work independently on your project.
  • You can ask for topics at the chair or you can suggest a topic by your own.

Milestones

You can talk to us about a potential thesis topic at any time during the semester. We first agree on a working title for your project that you can use to make yourself familiar with the relevant literature, methods, and data. You would also refresh your knowledge about R. This initial period can parallel with your attendance of one of our elective courses. The length of this period is flexible and depends, for instance, on student’s effort.

At the end of the initial period, there is an intermediate report about your project that you submit in the form of a presentation in our seminar (30 minutes). This is also when we agree on the final title of your thesis and officially notify the examination office about your thesis.

You then have 6 months to finish your thesis but good work during the initial phase might, of course, lead to a faster submission.

You have to hand in two copies of your thesis at the examination office. In addition you have to submit the electronic version of the thesis. For more information, please consult the examination regulations for our Master programs (especially §29).

After you submitted your thesis we will try to examine it quickly.

Current Topic Suggestions from the Chair

The suggested topics are based on the knowledge gained in our master’s-level courses “Multivariate Time Series Analysis” or “Bayesian Econometrics.”

All listed topics may be addressed by students in English or German, regardless of the language used in the project description provided here, as long as the examination regulations for the respective degree program permit the use of a specific language.

  • Building a Business Cycle Tracker Based on Machine Learning Techniques (building on Woloszko, 2024)
    [Understanding the current state of an economy is crucial for policymakers, businesses, and researchers, but traditional economic indicators like GDP are published at low frequency and often with significant delays. This thesis will explore how real-time, high-frequency data—such as Google search trends—can be used to estimate economic activity (or inflation) more accurately and promptly. The thesis will address key challenges, such as the short historical record of alternative data and their complex, non-linear relationship with macroeconomic indicatores. Combining approaches from the mixed data sampling (MIDAS) literature and machine learning techniques, the thesis will develop a new high-frequency index of economic activity. The topic is well-suited for students interested in empirical macroeconomics, forecasting, and economic policy analysis.]
  • The Macroeconomic Effects of Fragmentation (building on the index by Fernández-Villaverde et al., 2024)
    [After a period of globalization, the world economy is becoming more fragmented since the late 2010s and early 2020s. This topic is thus well suited in the newly emerging field of geoeconomics, which links economic with geopolitics. Which implications has a more fragmented world economy on trade and economic activity? This thesis will use and explore the causal effects of fragmentation on macroeconomic quantities. The exact research question can explore different dimensions and settings in international macroeconomics (e.g., geographical coverage: world economy, single- or multi-country settings; target variables: exchange rates, economic activity, etc.). The topic is well-suited for students interested in empirical macroeconomics, causal identification in macroeconomics, and time series analysis.]
  • The Macroeconomic Effects of Global Supply Chain Disruptions (building on Bai et al., 2024, or Finck and Tillmann, 2023)
    [The world economy is organized around an intricate global supply chain. Any sudden and large shocks to this global supply chain (e.g., the COVID-19 pandemic, or the Red Sea Crisis) might have ramifications for the world economy. This topic is thus well suited in the newly emerging field of geoeconomics, which links economic with geopolitics. This thesis will use and explore the causal effects of global supply chain disruptions on macroeconomic quantities. The exact research question can explore different dimensions and settings in international macroeconomics (e.g., geographical coverage: world economy, single- or multi-country settings; target variables: exchange rates, economic activity, etc.). The topic is well-suited for students interested in empirical macroeconomics, causal identification in macroeconomics, and time series analysis.]

Past Master’s Thesis Topics

The following master’s theses have been supervised by the chair since 2019. This list can help interested students get a sense of the types of topics that are suitable for a thesis at the Chair of Statistics and Econometrics.

  • Belgin Sentürk: Macroeconomic growth forecasting using Bayesian shrinkage priors.

  • Raj Sinha: Machine Learning Approach for Hiring Demand Forecasting in Large Scale Organizations
  • Hendrik Kirchmann: State-Dependent International Spillover Effects of U.S. Fiscal Policy Shocks

  • Benita Heid: Process Mining – die Zukunft der digitalen Prozessoptimierung
  • Vinzent Herdegen: Forecasting Foreign Trade Volumes Using Methods for Hierarchical Time Series
  • Gohar Grigoryan: The Effect of Oil Market Shocks on BRIC Stock Market Volatility over Time
  • Christoph Schuster: The socio-economic determinants of COVID-19: A spatial analysis of German county level data
  • Marlon Skawran: Identifizierung von Kfz-Schadenmustern mittels Clusteranalyse von Reparaturgutachten
  • Kathrin Engelhardt: Long-term predictability: Movements in volatility components

  • Johannes Frank: Forecasting Volatility of the S&P 500 during the COVID-19 Pandemic Using Neural Networks and Random Forests
  • Dominik Walter: Using multi-dimensional dynamic time warping to identify time-varying lead-lag relationships
  • Simone Merkle: Marktsegmentierung im B2B-Markt zur Ableitung strategischer Potenziale am Beispiel des Unternehmensmarktes der DATEV eG

  • Simon Rauch: Forecasting the Duration of Transshipment Processes Using Bayesian Networks
  • Philip Oberfichtner: Explainable Artifical Intelligence in der Versicherungsbranche. Die Identifikation von Risikomerkmalen am Beispiel von Zahntarifen

  • Fabian Waldow: Financial Machine Learning in Future Markets
  • Steffen Zierold: Robuste Portfolio-Optimierung für rangtransformierte Handelssignale
  • Denis Baev: Identification of Oil Price Shocks using the Narrative Sign Restruction Approach and Their Impact on Oil-Producing Economies