Syllabus

This course 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 consists of a lecture and 4 PC lab sessions (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.

This module covers basic concepts of machine learning as well as the most important algorithms for predictive and causal machine learning. Students get to know different machine and deep learning algorithms for learning statistical models in a data-driven way, know how to appropriately apply such algorithms for answering causal and predictive questions, and can implement these methods in R, Python, or other appropriate statistical software.

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

• Multi-tasking

• Ratchet effect

• Performance pay

• Tournament theory

• Intrinsic motivation and social motivators

The course introduces students to economic approaches for understanding and analysing human decision making, both in non-strategic and in strategic settings.

The first part of the course focuses on non-strategic situations. Students are introduced to expected utility theory (EUT), to Bayesian inference and to the difference between risk and ambiguity. We then discuss several systematic, empirical deviations from EUT, and discuss the pros and cons of Prospect Theory as an alternative theoretical framework.

The second part of the course aims at understanding decision making in strategic situations with the help of game theory. Students are exposed to a variety of typical strategic constellations and learn several useful solution concepts.

The third part of the course introduces students to applications of behavioural insights in public policy, e.g., redistribution, polarisation, and public health.

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.

In marketing, consumers and their behaviour are at the heart of all activities. This module introduces different tools to measure and analyse consumer behaviour, draw sound conclusions from such analyses, and translate these conclusions into a coherent marketing strategy. This includes topics such as measurement of customer value, customer selection, new product design and pricing. Additionally, the course covers methodologies that are used to evaluate marketing actions and explores the role of digitization and the use of big data in marketing.

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.

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.