Der Master-Studiengang ist in drei Modulblöcke und eine Master-Arbeit unterteilt und umfasst insgesamt 90 ECTS:
- Data Analytics (27 ECTS, 3 Pflichtmodule)
- Markets and Decision Making (21 ECTS, 3 Pflichtmodule)
- Applied Topics in Business and Economics (21 ECTS, drei von sechs Modulen nach Wahl). Sie können sich spezialisieren und entweder die drei Managementmodule oder die drei Wirtschaftsmodule belegen oder sich nicht spezialisieren und die Modulkombination, die Ihnen am besten gefällt, frei wählen
- Master-Arbeit (21 ECTS)
Die Liste der Module und das gesamte Programm sind auch in den Anhängen zum Studienreglement zu finden.
This course module discusses several of the most practically relevant econometric and statistical methods for empirical research and data analytics (e.g. in economics and social sciences) along with their underlying assumptions and properties. It also presents applications of these methods in real-world data using the statistical software “R (studio)”. The course module consists of a lecture and 4 PC lab sessions (quizzes). This course consists of asynchronous independent study periods, 5 online meetings, and 4 PC labs and quizzes. The topics covered in the lecture include:
1. The difference between causation (e.g. education has a causal effect on wage) and correlation (subjects with higher education have higher wages, but this may be driven by other factors than education as for instance ability); the intuition of experiments for assessing causation.
2. Linear regression (OLS - ordinary least squares) to assess the association of one or several variables (e.g. education, age,...) with an outcome of interest (e.g. wage).
3. Quantile regression to conduct empirical analyses at particular outcome ranks (e.g. for the median earner in the wage distribution).
4. Nonlinear regression (probit regression for binary outcomes like working vs. not working, tobit regression for censored outcomes).
5. Flexible methods for causal analysis (such as for evaluating the effect of training on earnings): matching, inverse probability weighting, doubly robust estimation.
6. Instrumental variable regression to assess the effect of endogenous variables (that are not random).
7. Regression discontinuity designs to assess the effect of a variable (e.g. health treatment) which is assigned based on a running variable (e.g. health index).
8. Panel data regression and “Differences-in-Differences” estimation when subjects are observed at several points in time.
9. Synthetic control methods for evaluating the effect of an intervention (e.g. of a health policy) on a single unit (e.g. a single region introducing the health policy).
10. Bootstrapping (resampling from the original data, e.g. for variance estimation).
11. Time series regression, e.g. for modeling stock prices or GDP growth over time.
Martin Huber: Causal Analysis. Impact Evaluation and Causal Machine Learning with Applications in R. The MIT Press (2023).
Jeffrey M. Wooldridge: Econometric Analysis of Cross Section and Panal Data. Second Edition. The MIT Press (2010).
Excerpts of both texts are available in electronic from (PDF) on the moodle platform of this module.
(Spring Semester 2024)
This course provides a user-friendly exploration of key issues and concepts in machine learning, with an emphasis on practical applications in economics and management. Students will understand the difference between predicting outcomes and understanding cause-and-effect relationships, delve into regularised regression techniques such as Lasso and Elastic Net, explore tree-based methods such as Random Forest, and learn about resampling methods such as bagging and cross-validation. The course also covers deep learning and unsupervised machine learning techniques. In addition, students will be introduced to a selection of advanced topics for example causal machine learning, reinforcement learning, GANs and LLMs. Using a hands-on approach and the industry-standard software R, the course facilitates the practical application of theoretical concepts, ensuring that students gain valuable skills in implementing machine learning models.
• Block 1: Introduction
• Block 2: Regularised Regresssion
• Block 3: Trees and Deep Learning
• Block 4: Unsupervised ML
• Block 5: Advanced ML Topics
Teaching videos and accompanying slides, exercises, codes and research papers will be posted on Moodle.
Main Textbook: “Introduction to Statistical Learning with Applications in R”, James Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani, download: www.statlearning.com
(Spring Semester 2024)
This module addresses practical issues of data analytics, e.g., data processing and data visualisation, and discusses empirical examples (or use cases) in a seminar format. To this end, various research projects might be assigned to different groups of students and the group work is to be presented in the course. The learning goal is that students get acquainted with practical issues of data analytics (such as data cleaning, plausibility checks, etc.) in a hands-on manner and learn to realise their own empirical projects by working through all steps from data acquisition and processing to the quantitative analysis and the interpretation of the results, using R, Python, or other appropriate statistical software.
A major part of economic activity takes place within or between organisations. In particular, many complex transactions can only be carried out with the involvement of organisations. It is thus crucial for economists and managers to understand the economic purpose of organisations, how organisations function, and how they should be designed.
The course consists of two parts. The first sheds light on the fundamental questions of why firms exist and what their boundaries should be. We cover classic and more recent theories of the firm to understand firm scope and to analyse decisions of vertical integration and outsourcing. The second part investigates the internal organisation of firms and how they should be designed to reach their goals. We rely on theories from economics, management, and related fields to analyse how explicit and implicit incentives affect performance and how organisations should design their incentive systems to motivate their employees. Across all topics, we rely on empirical evidence in the form of field data, experiments, and case studies to critically evaluate and, if possible, further develop the theories.
Key themes and concepts:
Part I – Foundations and Boundaries of Organisations:
• Incomplete contracts
• Relationship-specific investments and hold-up
• Transaction Cost Economics
• Property Rights Approach
• Contracts as Reference Points
Part II – Motivation Problem and the Internal Organisation of Firms:
• Principal-Agent model
• Ratchet effect
• Performance pay
• Tournament theory
• Intrinsic motivation and social motivators
Ellingsen, T. (2023). Institutional and Organizational Economics – A Behavioral Game Theory Introduction.
The book by Ellingsen as well as several research papers and additional book chapters will be provided via the course platform on Moodle.
(Automn Semester 2023)
The course introduces students to economic approaches for understanding, analysing, and evaluating human decision making.
Block I starts a selected collection of common bias and heuristics that are often observed in human decision making.
Block II introduces the economic foundations of decision making such as basic concepts of preference, axioms, states, acts, and outcomes. We will use decision tree to describe simple decisions under uncertainty, and discuss several systematic, empirical deviations from the expected utility theory (EUT).
Block III deals with decision making for future selves.
Block IV investigates decision making with strategic interactions. We will introduce game theory and a variety of typical strategic constellations as well as several useful solution concepts. Finally,
Block V presents organisational decision making and a touch of policy implications from natural experiments.
Teaching videos and accompanying research papers and book chapters will be posted on Moodle.
The case studies will be made available on a course page.
(Spring Semester 2024)
Entrepreneurship and innovation are key drivers of economic value creation and are becoming increasingly vital for industrialised nations. Thus, it is important that students are familiar with the most important concepts in innovation and are able to use essential tools to create innovation themselves. To achieve this, the module covers topics such as opportunity recognition for small firms, entrepreneurial decision making, teamwork in the entrepreneurial process, and characteristics as well as institutional conditions of successful start-ups.
Wahlmodule - Schwerpunkt Management
This module provides master students with a comprehensive introduction to marketing and consumer behavior. The conceptual foundations of marketing and consumer behavior form the core of the course. The course is structured around four different perspectives on marketing.
• Theoretical perspective
• Information-related perspective
• Strategic perspective
• Instrumental perspective (marketing mix)
Within this structure, current developments in marketing and consumer behavior are discussed (e.g., AI in marketing, current research findings), and different tools to measure and analyze consumer behavior are introduced in R exercises. It is demonstrated how you can draw sound conclusions from such analyses and translate these conclusions into a coherent marketing strategy.
Resources will be posted on Moodle:
• Teaching videos
• Research papers
• Exercises / case studies
• R scripts
• Supplementary materials (links, books, etc.)
(Spring Semester 2024)
This module covers different approaches to leadership from a management, economics, and psychological perspective. This entails topics such as foundations of leadership, leader effectiveness, leadership strategies and values, types of leadership, and control and delegation. Apart from methodological leadership issues the course introduces students to principles of responsible and ethical decision making which is a crucial capability in leadership positions.
The course Competitive and Corporate Strategy introduces students to the concept of competitive analysis and its implications for strategic decision making in firms. To enable students to analyse and establish the position of a firm within an industry, it covers essential topics and theories, such as the creation of competitive advantage, horizontal integration, and the resource-based view of the firm. After completing this course, students can identify, analyse, and synthesise data and information that supports company decision-making to optimise a firm’s strategic positioning.
Wahlmodule - Schwerpunkt Economics
We live in an increasingly digital world and economy. Information technologies have already transformed the way business is done and will continue to do so. This course covers basic theory in digital economics, for instance, network effects, value creation models, digital business models and market modelling.
The course introduces the most prominent theories and proposals related to economic growth and evaluates them critically from a sustainable development perspective. We analyse and model the relationships between economic, demographic, and environmental variables, and try to understand their implications for growth rates, inclusive growth, international development, and inequality.
This course focuses on the role of government policy in a market economy and on the challenges that arise in the context of the regulation of externalities and generating government revenue. It covers the theoretical and empirical evaluation of public policy as applied to a diverse set of important economic problems such as, for instance, firm concentration and market power, climate change and scarcity of natural resources, or inequality and redistribution.