Philosophy of Science (PoS)


This course gives both a theoretical overview on the foundations of philosophy of science and a hands-on introduction to practicing science and knowledge creation. In the first part we explore what science is, what its goals are, what it does, how it works, and what its basic assumptions are about knowledge, methods, the world, etc. We take a closer look at the processes involved in developing scientific knowledge/models; we follow the path from the phenomenon of interest, via the processes of observation, measuring, interpreting data, applying statistical methods, forming hypotheses, constructing scientific models/theories, making predictions and experimental designs, to finally "manipulating" the phenomenon of interest in an experiment (or simulation). We reflect on these processes from the perspective of students’ areas of specialization and taking into account their research questions.

Experimental and Simulation Methods (ESM)

Recommendation for attending this course:

• Successful attendance of the courses Management Decision Making and Multivariate Business Statistics, or in-depth knowledge of the contents of these courses.


This course gives an overview of simulation methods (first two sessions). Then, each student will discuss a scientific paper on a specific simulation approach with application to a certain field (e.g., marketing, organization, production, logistics, innovation and technology management, life sciences, health care). Students can choose from preselected papers. As students are highly encouraged to select a field in relation to their PhD-project, they might also choose another more relevant paper for them (third session). All participants will then outline ideas how to best implement this approach by anylogic (fourth session), and will finally present the implementation of the approach in anylogic (fifth session).

Management Decision Making (MDM)


The course covers main areas of decision theory at an advanced level. First, we analyze how preferences can be modeled and how multidimensional evaluation is related to dominance and efficiency. We use the expected utility theory for decisions under risk and consider applications and extensions to the concept. Then we look at the value of information. As the last part of the lecture, we cover multi-criteria decisions.

Selected References:

• Hadar, J. and W. R. Russell (1969). Rules for Ordering Uncertain Prospects. American Economic Review 59(1): 25-34.

• Hershey, J. C. and P. J. H. Schoemaker (1985). Probability versus certainty equivalence methods in utility measurement: Are they equivalent? Management Science 31(10): 1213-1231.

• Kahneman, D. and A. Tversky (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica 47 263-291.

• Roy, B. (1996). Multicriteria Methodology for Decision-Aiding. Kluwer Academic Publishers, Dordrecht.

• Saaty, T. L. (1980). The Analytic Hierarchy Process. New York, McGraw-Hill.

• Starmer, C. (2000). Developments in Non-Expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk. Journal of Economic Literature 38(2): 332-382.

• Tversky, A. and D. Kahneman (1986). Rational Choice and the Framing of Decisions. Journal of Business 59(4): 251-278.

• Wickham, P. A. (2003). The representativeness heuristic in judgements involving entrepreneurial success and failure. Management Decision 41(1/2): 156-167.

Management Control (MC)

Recommendation for attending this course:

• Successful attendance of the course Management Decision Making, or in-depth knowledge of the contents of that course.


The course introduces contract theory as a methodological tool for analyzing personnel, organizational, financial and public economics problems. The ultimate course goal is twofold:

a) generally, students achieve an understanding of contract theory as modeling tool within their fields of application;

b) specifically, interested students learn the basic framework to develop their own contractual models and/or, respectively, empirical investigations of contract theoretical models.

Selected References:

• Bolton, P. und M. Dewatripont (2005). Contract Theory, Cambridge, MA, and London, UK.

• Laffont, Jean-Jacques and David Martimort (2002). The Theory of Incentives: the principal-agent model, Princeton, NJ, and Woodstock, UK.

• Salanie, B. (1997). The Economics of Contracts: A Primer, Cambridge, MA, and London, UK.

Advanced Microeconomics (AME)

Recommendation for attending this course:

• Knowledge of statistics and applied microeconometrics (MA course “Microeconometrics” or equivalent)

• Basic knowledge of Stata or similar statistical software


This course provides students with a thorough and comprehensive knowledge of microeconometrics. Building upon the introductory course of microeconometrics, students will get a deeper understanding of microeconometric theory and the relevant methods used in contemporary empirical analysis. While the emphasis of the course is on estimating causal effects, other complementary techniques which are necessary to deal with specific data structures will be discussed as well. Using the statistical software Stata, students will learn to apply the introduced methods in regular computer practice sessions.

Questions addressed in this course are, among others: What methods allow to estimate causal effects? Which techniques are suitable for the analysis of categorical outcomes or duration outcomes? What are the implications of missing data or measurement error? How can the discussed estimation methods be implemented in Stata? etc.

Selected References:

• Cameron, A. Colin, and Pravin K. Trivedi. Microeconometrics: Methods and Applications. Cambridge University Press, 2005

• Wooldridge, Jeffrey M. Econometric Analysis of Cross Section and Panel Data (2nd edition). MIT Press, 2010

• Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press, 2008

Multivariate Business Statistics (MBS)


The course consists of three parts. In the first part, we will cover the theoretical principles of selected multivariate techniques. Students will be expected to complement these classes through individual literature studies of text book material, and they will sit a written test covering the sessions of this first part of the class. In the second part of the course, students will present an empirical article, and they will be encouraged to discuss the techniques used by the authors of that paper (mid-term presentations). Finally, in part three of the class, students will conduct a practical data analysis project with a given data set. For data analysis, we deploy the PASW (SPSS).

Selected References:

• Backhaus, K. et al. (2011). Multivariate Analysemethoden, 13. Aufl., Springer.

• Hair, J.F.Jr., Black, W.C., Babin, B.J., Anderson, R.E. (2010): Multivariate Data Analysis, 7th ed., Prentice Hall.

• Iacobucci, D., Churchill, G.A.Jr., (2010). Marketing Research – Method. Foundations, 10th ed., South-Western.

• Tabachnik, B.G., Fidell, L.S. (2007). Using Multivariate Statistics, 5th ed., Pearson/Allyn&Bacon.

Structural Equation Modeling (SEM)

Recommendation for attending this course:

• Successful attendance of the course Management Decision Making, or in-depth knowledge of the contents of that course.


The course seeks to provide a user‐friendly introduction to structural equations modeling (SEM) using the LISREL program. It is designed for non‐experts and the emphasis is on understanding and applying SEM as a tool in substantive research. The course is designed for PhD students and assumes previous knowledge of data analysis and statistics (including factor analysis and regression). Students taking this course must have already successfully completed the Management Decision Making and Multivariate Business Statistics courses of the PhD Management core program.

Selected References:

• Bagozzi, R. P. & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1): 74‐94.

• Bollen, K. A. & Lennox, R. (1991). Conventional Wisdom on Measurement: A Structural Equation Perspective. Psychological Bulletin, 110 (2): 305‐314.

• Churchill, G. A. (1979). A Paradigm for Developing Better Measures of Marketing Constructs. Journal of Marketing Research, 16: 64‐73.

• Diamantopoulos, A. & Winklhofer, H. (2001). Index Construction with Formative Indicators: An Alternative to Scale Development. Journal of Marketing Research, 38 (2): 269‐277.

• Diamantopoulos, A. and Siguaw, J.A. (2000). Introducing LISREL, Sage Publications (ISBN 0‐7619‐5171‐7).

• Gefen, D., Straub, D. W. & Boudreau, M‐C. (2000). Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems, 4 (7): 1‐79.

• Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010). Multivariate Data Analysis, 7th edition, Pearson.

• Medsker, G. J., Williams, L. J. & Holahan, P. J. (1994). A Review of Current Practices for Evaluating Causal Models in Organizational Behavior and Human Resources Management Research. Journal of Management, 20 (2): 439‐464.

Econometrics (ECR)


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.

Applying Advanced Regression Techniques in Management (ARTM)

Recommendation for attending this course:

• Successful attendance of the course Econometrics, or in-depth knowledge of the contents of that course.


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.