Elective courses PhD in Management

Students are required to take at least two elective courses – and they are encouraged to co-ordinate on their choices with their supervisors. Students may attend more than two courses, however, and they should be prepared to take up to six elective classes depending on their prior training and their supervisor’s recommendation. Some of the elective classes are offered on a (rather) regular basis as part of the PhD program in management by business faculty themselves. Other classes that may count as electives are held at the research Master level at the Departments of Economics, Psychology, or Computer Science at the University. Below, find a (non-exhaustive) list of courses that we consider most relevant as elective courses for our PhD students:


Advanced Microeconomics (AM)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of at least three core courses

• Successful attendance of BEE (or similar class)

• Acknowledgement of the class as a PhD-relevant elective by the supervisor and the Head of the PhD program (happens on an individual case-by-case basis depending on prior training of the student)

• Admission to the class by the instructor of the course

Contents:

The course provides master students with the basic tools of microeconomic analysis. Students should learn how to handle the basic model of market competition, price determination, and welfare analysis. Particular attention is paid to market failures, the exercise of market power, and dealing with missing information in markets.

Selected References:

• Frank, R. H. (1991). Microeconomics and behavior. New York: McGraw-Hill.

• Gravelle, H., & Rees, R. (2004). Microeconomics. New York: Pearson Education.

• Jehle, G. A., & Reny, P. J. (2001). Advanced microeconomic theory. Boston: Addison-Wesley.

• Mas-Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic theory. New York: Oxford University Press.

Applying Advanced Regression Techniques in Management (ARTM)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of at least three core courses

• Successful attendance of the course Econometrics

Contents:

This course seeks to complement “Multivariate Business Statistics” for PhD students in three distinct ways.

1. It will introduce to more complex limited dependent variable problems

2. It will introduce to the analysis of panel data

3. It will introduce to programming with one of the most powerful software tools in econometrics, namely STATA.

This course seeks also to complement “Econometrics” for PhD students in another important way. Namely,

4. the course will offer you an additional opportunity to become ever more familiar with the “hands on” application of both basic and more advanced regression techniques for your own research purposes.

The focus of the course is “solid application”. Hence, neither our theory sessions nor any of the exercises will be centred on mathematical proofs but rather on a proper understanding of the logic, options, and caveats of the methods we discuss.

One focus of this class will be on getting you to work on applied problems yourself. Essentially, the course will follow a “sandwich format” where front-end theory sessions, alternate with student presentations on selected research articles, and computer sessions during which we work on simulated and real data.

Selected References:

• Foss, N. J., & Laursen, K. (2005). Performance pay, delegation and multitasking under uncertainty and innovativeness: An empirical investigation. Journal of Economic Behavior and Organization, 58, 2, 246-276.

• Gulati, R., & Singh, H. (1998). The Architecture of Cooperation: Managing Coordination Costs and Appropriation Concerns in Strategic Alliances. Administrative Science Quarterly, 43, 4, 781-814.

• Henkel, J. (2006). Selective revealing in open innovation processes: The case of embedded Linux. Research Policy, 35, 7, 953-969.

• Henkel, J., & Reitzig, M. (2008). Patent Sharks. Harvard Business Review, 86, 6, 129-133.

• Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge [Cambridgeshire: Cambridge University Press.

• Mukherjee, A. S., Lapre, M. A., & Van Wassenhove, L. N. (1998). Knowledge Driven Quality Improvement. Management Science, 44.

• Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, Mass: MIT Press.

• Wooldridge, J. M. (2002). Introductory econometrics: A modern approach. Princeton, N.J.

Behavioral and Experimental Economics (BEE)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of at least three core courses

• Acknowledgement of the class as a PhD-relevant elective by the supervisor and the Head of the PhD program (happens on an individual case-by-case basis depending on prior training of the student)

• Admission to the class by the instructor of the course.

Contents:

Behavioral economics attempts to make economics a more relevant and powerful science of human behavior by integrating insights from psychology and the social sciences into economics. Experimental economics adapts methods developed in the natural sciences to study economic behavior. Experiments are valuable in testing to what extent the integration of insights from other disciplines into economics is necessary and fruitful.

Behavioral and Experimental Economics is a vibrant field of research in economics and sheds new light on many old and important issues in economics. The field has recently received wide recognition, for example by the award of the Nobel Prize in Economics 2002 to Daniel Kahneman and Vernon Smith. The field is rapidly growing. This course can therefore not provide a comprehensive overview but concentrates on selected topics instead.

Selected References:

• De Martino, B., Kumaran, D., Seymour, B. and Dolan, R.J. (2006). Frames, Biases, and Rational Decision-Making in the Human Brain. Science 313: 684-7.

• Falk, A. and Heckman, J. (2009). Lab Experiments Are a Major Source of Knowledge inthe Social Sciences. Science 326(5952): 535-8.

• Gächter, S., Thöni, C. and Tyran, J.-R. (2006). Cournot Competition and Hit-and-Run Entry and Exit in a Teaching Experiment. Journal of Economic Education 37(4): 418-30.

• Gneezy, U. and Potters, J. (1997). An Experiment on Risk Taking and Evaluation Periods. Quarterly Journal of Economics 112(2): 631-45.

• Gneezy, U., Kapteyn, A. and Potters, J. (2003). Evaluation Periods and Asset Prices in a Market Experiment. Journal of Finance 58(2): 821-37.

• Gode, D.K. and Sunder, S. (1993). Allocative Efficiency of Markets with Zero-Intelligence Traders: Markets as a Partial Substitute for Individual Rationality. Journal ofPolitical Economy 101(1): 119-37.

• Huck, S. and Normann, H.-T. and Oechssler, J. (2004). Two are Few and Four are Many: Number Effects in Experimental Oligopolies. Journal of Economic Behavior and Organization 53(4): 435-46.

• Wolfers, J. and Zitzewitz, E. (2004). Prediction Markets. Journal of Economic Perspectives 18(2): 107-26.

Designing and Implementing an Economic Experiment (DIEE)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of at least three core courses

• Successful attendance of BEE (or similar class)

• Acknowledgement of the class as a PhD-relevant elective by the supervisor and the Head of the PhD program (happens on an individual case-by-case basis depending on prior training of the student)

• Admission to the class by the instructor of the course

Contents:

Experimental economics has recently become a popular research method for understanding all aspects of individual and group economic behavior. Economists use laboratory and field experiments to test the validity of economic theories and efficiency of market mechanisms. Knowing how to properly design and implement an economic experiment has thus become increasingly important. The idea of the seminar is to develop the research skills required in the field of Experimental Economics. Students will become familiar with the following aspects of experimental research: (1) how to develop a good research question, (2) how to design and implement an experiment to address the posed research question, (3) how to analyze experimental data and making conclusions justified by statistical evidence. By the end of the course students will have completed and experimental research project.

To be enrolled in the course, students need to have taken a class providing a solid introduction into the field, for example "Behavioral and Experimental Economics" (UK 040832). Students with comparable backgrounds can also be admitted but need to provide evidence that their knowledge is comparable. PhD students with an appropriate background are also welcome to attend the class. Successful completion of the course earns students 8 ECTS credits.

Selected References

• Kosfeld, M. (2004). Economic networks in the laboratory: A survey. Review of Network Economics, 3:20-42

• Abbink. K. (2005). Laboratory Experiments on Corruption in The Handbook of Corruption, ed. by S. Rose-Ackerman, Edward Elgar Publishers, Cheltenham, UK and Northampton, US.

• Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets. Econometrica: Journal of the Econometric Society, 56, 5.

• Powell, O. (2010). Essays on experimental bubble markets. Tilburg.

• Levine, D., Palfrey, (2007). The Paradox of Voter Participation? A Laboratory Study. American Political Science Review, 101, 1, 143-158.

Econometrics (ECR)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of at least three core courses

Contents:

This course provides a basic working knowledge of econometrics for students who had one or more undergraduate econometrics courses. Students will be introduced to the major quantitative techniques that economists use to test models, study economic behavior, evaluate policies, and relationships between variables Topics include dynamic linear regression models, autoregressive models, instrumental variables estimation, and systems of simultaneous equations. These models are widely used in the empirical literature, and a good understanding of these models is crucial for students.

Selected References:

• Baltagi, B. H. (2011). Econometrics. Berlin: Springer.

• Brockwell, P. J., & Davis, R. A. (1991). Time series: Theory and methods. New York, Berlin: Springer- Verlag.

• Davidson, R., & MacKinnon, J. G. (1993). Estimation and inference in econometrics. New York: Oxford University Press.

• Greene, W. H. (1990). Econometric analysis. New York: Macmillan.

• Maddala, G. S. (1977). Econometrics. New York: McGraw-Hill.

• Judge, G. G. (1988). Introduction to the theory and practice of econometrics. New York: Wiley.

• Kelejian, H. H., & Oates, W. E. (1989). Introduction to econometrics: Principles and applications. New York: Harper & Row.

• Poetscher,B.M., Prucha,I.R. (2000). Basic Elements of Asymptotic Theory, in: Companion in Theoretical Econometrics, B.Baltagi (ed), Blackwell Publ., 2000.

• Ruud, P. A. (2000). An introduction to classical econometric theory. New York: Oxford University Press.

Experimental IO and Economics (EIOE)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of at least three core courses

Contents:

The principal aim of this course is to enable and help students to develop their own experimental projects that could later actually be run in an experimental lab. For this purpose, the course will provide an introduction to the methods of experimental economics and will consist of two parts. In the first part, methods of experimental economics with an emphasis on principles of economic experiments and experimental design will be discussed. Here the focus will be on a range of experimental design issues and practical advice. Also in the first part, a number of selected experimental papers will be discussed, again emphasizing method and design. In the second part of the course, students will be asked to present their own experimental projects that they started to develop during the first part of the course and which will be thoroughly discussed in class. At the end of the course/semester, students will have to submit a document describing their experimental projects, providing details on the research question, background / related literature, hypotheses to be tested by the experiment, design of the experiment, sample instructions, and intended methods for data analysis.

Selected References:

• Friedman, D., & Shyam, S. (1994). Experimental methods: A primer for economists. Cambridge

• Friedman, D., Cassar, A., & Selten, R. (2004). Economics lab: An intensive course in experimental economics. London: Routledge.

• Bardsley, N. (2010). Experimental economics: Rethinking the rules. Princeton, N.J: Princeton University Press.

• Davis, D. D., & Holt, C. A. (1993). Experimental economics. Princeton, N.J: Princeton University Press.

• Kagel, J. H., & Roth, A. E. (1995). The handbook of experimental economics. Princeton, N.J: Princeton University Press.

• Plott, C. R., & Smith, V. L. (2008). Handbook of experimental economics results. Amsterdam: North-Holland.

Game Theory (GT)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of at least three core courses (including MDM)

• Acknowledgement of the class as a PhD-relevant elective by the supervisor and the Head of the PhD program (happens on an individual case-by-case basis depending on prior training of the student)

• Admission to the class by the instructor of the course

Contents:

The objective of this course is to learn how to master game theory. Game theory is the theory of making decisions when outcomes are influenced by others making decisions. Games will be played in class to help gain intuition. There will be real life examples (such as auctions, market entry, public good provision) but the main emphasis is on the methodology, the mathematics of strategic decision making. Topics we will be covering include

1. Utility, uncertainty, risk, decision making and rationality

2. Games, strategies and timing

3. Dominance, iterated dominance, rationalizability

4. Extensive form games with perfect information, backwards induction

5. Nash equilibrium

6. Subgame perfection, forwards induction

7. Repeated games, folk theorem

8. Bayesian games

No prerequisites, however if you have never attended a game theory course then you are strongly advised to read some basic material before the course, eg Kokesen & Ok (2007). You will also need to understand decision making under uncertainty and expected utility theory. It is not mandatory, however you are strongly advised to register also for the UE Game Theory 2 tutorial (4 ECTS) held by Mariya Teteryatnikova. These are practice sessions relating to the material of this lecture, and they are extremely useful for learning how to solve the exams of this course (in which you will not be given practice problem sets).

Selected References:

• Kokesen, L. and E. Ok. (2007). An Introduction to Game Theory. Online lecture notes

• Fudenberg, D. and J. Tirole. (1991). Game Theory. MIT Press

• Mas-Colell, A., M.D. Whinston and J.R. Green. (1995). Microeconomic Theory. Oxford University Press (only selected chapters)

Theory of Networks (ToN)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of at least three core courses

Contents:

The course provides a discussion of the theoretical foundation of networks (strategic alliances, joint ventures, franchising, licensing, consortia, clusters, virtual networks). It emphasizes the relationships between different theories and networks. The sessions provide an overview of a number of the major theoretical and methodological approaches adopted in network research as it evolved into a specific research field. The course incorporates sessions on essential aspects of network research including transaction cost economics, property rights theory, information economics, resource-based theory, real options reasoning and the relational view of networks. In particular, the course highlights current research challenges and methodological issues facing the research in economics and management of networks and encourages a discussion among the participants to determine what constitutes an appropriate future research strategy, especially applied to your PhD-project.

Selected References:

• Geyskens et al. (2006). Make, Buy or Ally, Academy of Management Journal, 49, 519 – 43.

• Mayer, K.J. and Salomon, R.M. (2006). ‘Capabilities, contractual hazards, and governance: Integrating resource-based and transaction cost perspective’. Academy of Management Journal, 49, 942-959.

• Akerlof GA. (1970). The market for 'lemons': quality uncertainty and the market mechanism. Quarterly Journal of Economics 84: 488-500.

• Reuer JJ, Ragozzino R. (2006). Agency hazards and alliance portfolios, Strategic Management Journal, 27, 27 – 43.

• Zaheer, A. and Venkatraman, N. (1995). ‘Relational governance as an interorganizational strategy: an empirical test of the role of trust in economic exchange’. Strategic Management Journal, 16, 373–92.

• Lazzarini, S. G., G. J. Miller, T. R. Zenger (2008). Dealing with the Paradox of Embeddedness: The Role of Contract and Trust in Facilitating Movement out of Committed Relationship, Organization Science, 19, 709 – 728.