# Course Catalog

## Fall 2015

### Lecturer(s)

### Course Content

This course is designed to guide doctoral students in the usage of methods and tools in empirical research in accounting and finance, and bring them quickly to the level at which they can "technically" implement empirical research. Selected topics include:

- Typical steps in emp. projects
- Alternative data sources
- Databases in Accounting & Finance
- Programming (SAS, STATA)
- The publication process
- Discussion of replication projects

For further information please contact Ferdinand Elfers: felfers(at)mail.uni-mannheim.de

### Schedule

**Lecture**

### Prerequisites

basic mathematical knowledge

### Course Content

The course consists of four chapters:

- Chapter 1: basic mathematical concepts like sets, functions and relations are introduced and discussed. Strict mathematical reasoning is explained and applied.
- Chapter 2: covers the concept of metric and normed spaces and discusses the convergence of sequences in these spaces, the continuity of functions, and the concept of compact sets.
- Chapter 3: deal with vector spaces. matrix algebra, linear transformation, and eigenvalues of matrices.
- Chapter 4: covers a multivariate concept of differentiability and its application in solving unconstraint and constrained optimization problems.

**Requirements for the assignment of ECTS Credits and Grades:**

- Exam (120 min)

### Competences acquired

The students know basic mathematical concepts of analysis and linear algebra. They can interpret mathematical formulas that are written in the condensed mathematical syntax. The students understand the concept of a proof and can develop rigorous mathematical proofs in a elementary level. They understand abstract mathematical concepts like metric spaces and linear spaces and are able to comprehend argumentation on basis of abstract mathematical concepts. They are able to apply their knowledge; especially they are familiar with the calculation of limits and derivatives, the methods of linear algebra, and they can solve nonlinear optimization problems. The students are able to communicate their mathematical knowledge in English.

### Schedule

**Lecture**

**Tutorial**

### Prerequisites

E700

### Course Content

The course gives a foundation for studies for microeconomics at the PhD level. The first part is devoted to consumer and producer theory. It is organized as follows:

1. Choice, preference and utility

2. Structural properties of preferences and utility functions

3. Basics of consumer demand

4. Expenditure minimization

5. Classical demand theory

6. Competitive and profit-maximizing firms

7. Consumer and producer surplus

8. Choice under uncertainty

9. Utility for money

The second part covers game theory and is organized as follows:

10. Static games of complete information: Rationalizability and iterated strict dominance

11. Static games of complete information: Nash equilibrium

12. Static games of incomplete information

13. Dynamic games: The extensive form

14. Dynamic games: Equilibrium concepts

**Teaching method**

Lecture (3 SWS), Exercise (1.5 SWS)

**Requirements for the assignment of ECTS-Credits and Grades**

- Written exam: 120 min (90% weighting)
- Exercises (10% weighting)

**Literature**

Recommended textbooks:

- Fudenberg, D & Tirole, J. (1991). Game Theory. MIT Press
- Kreps, D. (2012). Microeconomic Foundation 1: Choice and Competitive Markets. Princeton University Press.
- Mas- Colell, A. Whinston, M.D. & Green, J. (1995). Microeconomic Theory. Oxford University Press.
- Osborne M. and Rubinstein, A. (1994): A Course in Game Theory. MIT Press

### Competences acquired

Students learn the basic tools for graduate level microeconomic analysis. The concepts learned in the course serve as building blocks for more advanced topics such as the ones studied in Advanced Microeconomics 2 and 3 and also for macroeconomics and empirical studies. Students also learn using rigorous formal proofs for microeconomic questions.

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Prerequisites

E700

The course is intended for Masters and first year PhD students with prior knowledge of undergraduate level econometrics. Working knowledge of basic probability theory, differential calculus, linear algebra and matrix algebra are assumed. Students should check if they are sufficiently familiar with these topics.

### Course Content

The course is designed to offer an advanced treatment to econometric theory and applications. Topics covered include: Repetition of ordinary least squares and generalized least squares, instrumental variables estimation, simultaneous equations, generalized method of moments and maximum likelihood estimation, time series and panel data econometrics. Attendance in the lectures and exercise sessions are mandatory. Attempting exercise questions ahead of each session and taking active part during the course of the sessions is essential.

Rooms for Thursday seminars:

08.10.15 – SO 133

15.10.15-12.11.15 – O 131

19.11.15-10.12.15 – SO 133

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Course Content

his course aims to provide a working knowledge of basic probability theory and inductive statistics. The course is especially recommended for students wanting to refresh the skills required to attend the course Advanced Econometrics I (E703). The topics roughly align with appendices B, C, and D of the book Econometric Analysis by William H. Greene (2008, 6th ed.), for example: random variables, expectations, probability distributions, random sampling, point estimators, confidence intervals,, hypothesis testing, large sample distribution theory.

Background reading material:

- Greene, W. H., Econometric Analysis. Upper Saddle River: Pearson Prentice Hall, 2008.
- Introduction to Econometrics by Stock and Watson (2007, 2nd ed.), chapters 2 and 3.
- Introduction to Probability Models by Ross (2000, 2nd ed.), chapters 2.1-2.5, 2.7, and 3.1-3.4

Please note that the Statistics Refresher course will cover integrals and most of the basic statistics you’ll need in Advanced Econometrics I. These topics won’t be covered again in Advanced Econometrics I. Hence you are advised to attend the Statistics Refresher course, if you have some doubts about your knowledge regarding the above mentioned topics.

### Schedule

**Lecture**

### Course Content

This course aims at students in accounting and taxation. The course is taught in a seminar-style format. Students present their own research and discuss the presentations of other students. Students are introduced in writing referee reports to (drafts of) papers. Allocation of topics will be determined in class.

Students will learn how to present and discuss their own research results. They will become acquainted with acting as discussant for other topics. Additionally, they will learn how to write a referee report.

### Schedule

**Lecture**

### Lecturer(s)

### Prerequisites

The course is intended for PhD students with some prior knowledge of undergraduate level econometrics and statistics.

### Course Content

The course enables students to apply the econometric methods which are commonly used in economic research. Special attention is given to the interpretation of empirical results and understanding the potential caveats of different approaches. Topics covered include: Ordinary least squares, instrumental variables estimation, and panel data econometrics. Further topics may also be included according to demand by participants. Attendance in the lectures and exercise sessions are mandatory. Attempting exercise questions ahead of each session and taking active part during the course of the sessions is essential.

### Schedule

**Lecture**

### Prerequisites

basic mathematical knowledge

### Course Content

The course consists of four chapters:

- Chapter 1: basic mathematical concepts like sets, functions and relations are introduced and discussed. Strict mathematical reasoning is explained and applied.
- Chapter 2: covers the concept of metric and normed spaces and discusses the convergence of sequences in these spaces, the continuity of functions, and the concept of compact sets.
- Chapter 3: deal with vector spaces. matrix algebra, linear transformation, and eigenvalues of matrices.
- Chapter 4: covers a multivariate concept of differentiability and its application in solving unconstraint and constrained optimization problems.

**Requirements for the assignment of ECTS Credits and Grades:**

- Exam (120 min)

### Competences acquired

The students know basic mathematical concepts of analysis and linear algebra. They can interpret mathematical formulas that are written in the condensed mathematical syntax. The students understand the concept of a proof and can develop rigorous mathematical proofs in a elementary level. They understand abstract mathematical concepts like metric spaces and linear spaces and are able to comprehend argumentation on basis of abstract mathematical concepts. They are able to apply their knowledge; especially they are familiar with the calculation of limits and derivatives, the methods of linear algebra, and they can solve nonlinear optimization problems. The students are able to communicate their mathematical knowledge in English.

### Schedule

**Lecture**

**Tutorial**

### Prerequisites

E700

### Course Content

The course gives a foundation for studies for microeconomics at the PhD level. The first part is devoted to consumer and producer theory. It is organized as follows:

1. Choice, preference and utility

2. Structural properties of preferences and utility functions

3. Basics of consumer demand

4. Expenditure minimization

5. Classical demand theory

6. Competitive and profit-maximizing firms

7. Consumer and producer surplus

8. Choice under uncertainty

9. Utility for money

The second part covers game theory and is organized as follows:

10. Static games of complete information: Rationalizability and iterated strict dominance

11. Static games of complete information: Nash equilibrium

12. Static games of incomplete information

13. Dynamic games: The extensive form

14. Dynamic games: Equilibrium concepts

**Teaching method**

Lecture (3 SWS), Exercise (1.5 SWS)

**Requirements for the assignment of ECTS-Credits and Grades**

- Written exam: 120 min (90% weighting)
- Exercises (10% weighting)

**Literature**

Recommended textbooks:

- Fudenberg, D & Tirole, J. (1991). Game Theory. MIT Press
- Kreps, D. (2012). Microeconomic Foundation 1: Choice and Competitive Markets. Princeton University Press.
- Mas- Colell, A. Whinston, M.D. & Green, J. (1995). Microeconomic Theory. Oxford University Press.
- Osborne M. and Rubinstein, A. (1994): A Course in Game Theory. MIT Press

### Competences acquired

Students learn the basic tools for graduate level microeconomic analysis. The concepts learned in the course serve as building blocks for more advanced topics such as the ones studied in Advanced Microeconomics 2 and 3 and also for macroeconomics and empirical studies. Students also learn using rigorous formal proofs for microeconomic questions.

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Prerequisites

E700

The course is intended for Masters and first year PhD students with prior knowledge of undergraduate level econometrics. Working knowledge of basic probability theory, differential calculus, linear algebra and matrix algebra are assumed. Students should check if they are sufficiently familiar with these topics.

### Course Content

The course is designed to offer an advanced treatment to econometric theory and applications. Topics covered include: Repetition of ordinary least squares and generalized least squares, instrumental variables estimation, simultaneous equations, generalized method of moments and maximum likelihood estimation, time series and panel data econometrics. Attendance in the lectures and exercise sessions are mandatory. Attempting exercise questions ahead of each session and taking active part during the course of the sessions is essential.

Rooms for Thursday seminars:

08.10.15 – SO 133

15.10.15-12.11.15 – O 131

19.11.15-10.12.15 – SO 133

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Course Content

The aim of this course is to provide Ph.D. students with the foundations of financial economics in a rigorous way. The course covers choice under uncertainty and utility theory, portfolio theory and capital market equilibrium (CAPM and APT). The focus of the course will be on the consumption-based approach to asset pricing. We will introduce concepts such as the stochastic discount factor (or pricing kernel), contingent claims and risk-neutral valuation, and beta representations or factor pricing models. We will study single- and multi-period consumption-based models and look at the role of information for asset pricing.

Evaluation:

- Term paper (paper, presentation, discussion): 50%
- Final exam: 50%

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

his course aims to provide a working knowledge of basic probability theory and inductive statistics. The course is especially recommended for students wanting to refresh the skills required to attend the course Advanced Econometrics I (E703). The topics roughly align with appendices B, C, and D of the book Econometric Analysis by William H. Greene (2008, 6th ed.), for example: random variables, expectations, probability distributions, random sampling, point estimators, confidence intervals,, hypothesis testing, large sample distribution theory.

Background reading material:

- Greene, W. H., Econometric Analysis. Upper Saddle River: Pearson Prentice Hall, 2008.
- Introduction to Econometrics by Stock and Watson (2007, 2nd ed.), chapters 2 and 3.
- Introduction to Probability Models by Ross (2000, 2nd ed.), chapters 2.1-2.5, 2.7, and 3.1-3.4

Please note that the Statistics Refresher course will cover integrals and most of the basic statistics you’ll need in Advanced Econometrics I. These topics won’t be covered again in Advanced Econometrics I. Hence you are advised to attend the Statistics Refresher course, if you have some doubts about your knowledge regarding the above mentioned topics.

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

This course covers a broad range of popular topics in the area of Macro-Finance with a particular emphasis on empirical studies on the research frontier. The topics include but are not limited to return predictability, return anomalies, Campbell-Shiller decomposition, volatility risk, long-run risks, and macroeconomic determinants of time-varying risk premia in equity and foreign exchange markets. Students will be trained to conduct independent empirical analysis and gain experience in designing, executing, and reporting research. The course consists of an individual track, i.e. completing an empirical project and writing a term paper, and an interactive track, i.e. participating in plenary sessions and meetings with the instructor. A detailed syllabus with topic description and available data sources will be made available in the beginning of the course.

The evaluation is based on the quality of the term paper, discussion of another paper, and participation in class.

### Schedule

**Lecture**

### Prerequisites

basic mathematical knowledge

### Course Content

The course consists of four chapters:

- Chapter 1: basic mathematical concepts like sets, functions and relations are introduced and discussed. Strict mathematical reasoning is explained and applied.
- Chapter 2: covers the concept of metric and normed spaces and discusses the convergence of sequences in these spaces, the continuity of functions, and the concept of compact sets.
- Chapter 3: deal with vector spaces. matrix algebra, linear transformation, and eigenvalues of matrices.
- Chapter 4: covers a multivariate concept of differentiability and its application in solving unconstraint and constrained optimization problems.

**Requirements for the assignment of ECTS Credits and Grades:**

- Exam (120 min)

### Competences acquired

The students know basic mathematical concepts of analysis and linear algebra. They can interpret mathematical formulas that are written in the condensed mathematical syntax. The students understand the concept of a proof and can develop rigorous mathematical proofs in a elementary level. They understand abstract mathematical concepts like metric spaces and linear spaces and are able to comprehend argumentation on basis of abstract mathematical concepts. They are able to apply their knowledge; especially they are familiar with the calculation of limits and derivatives, the methods of linear algebra, and they can solve nonlinear optimization problems. The students are able to communicate their mathematical knowledge in English.

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Course Content

This course is designed for doctoral students in information systems and operations/logistics. It provides a basic understanding of philosophy of science and its epistemological foundations. On the one hand, the course will focus on those concepts which derive knowledge from observation, induction, and refutation of facts. Furthermore, it also takes experiments as well as the new experimentalism into account in order to refer to those disciplines that focus on the evaluation of artifacts like prototypes and algorithms for example. Thus, the underlying epistemological foundations are of central interest to every doctoral students who studies the structure and behavior of information systems and operations/logistics phenomena. The course will be offered in an interactive style. All doctoral students have to offer at least one presentation and a documentation regarding a specific epistemological stance. Furthermore, participants have to discuss an article from literature in order to apply and reinforce the epistemological stance presented. Assignment of topics will be conducted by the lecturer.

Introductory literature:

- Chalmers, A.F.: What is this thing called science? 4th edition, Hill and Wang Publisher, 2013.

or

- Chalmers, A.F.: Wege der Wissenschaft: Einführung in die Wissenschaftstheorie (German Edition), Springer 2006

These two books represents a starting point. They will be the basis for the discussion sessions with the instructor.

Recommendable is also the following book for further studies:

- Curd, M.; Cover, J.A.: Philosophy of Science - the Central Issues, 2nd edition, Norton publishers, 2012

For session 6, the following sources are recommended:

- Von Bertalanffy, L., “The History and Status of General Systems Theory”, The Academy of Management Journal, Vol. 15, No. 4, 1972.
- Simon, H.A., “The sciences of the artificial”, MIT Press, Cambridge, 1996. [Special focus on chapter 5: “The Science of Design: Creating the Artificial”]

You are requested to retrieve additional literature which deepens this respective stances. This will also guide your seminar paper.

### Lecturer(s)

### Prerequisites

Recommended: Fundamentals in mathematics (including Linear Programming)

### Course Content

This course aims at PhD students in information systems, business administration, and computer science. It provides a basic understanding of optimization problems and methods. The course is taught in a seminar-style format. Allocation of topics will be done together in the class.

### Competences acquired

The course aims to introduce the students to fundamental linear and combinatorial optimization problems. They learn to formulate optimization models as mixed-integer linear programs, how to construct heuristics, and how to analyze the performance of heuristic algorithms. The students learn to deal with the complexity of real-world problems via aggregation, relaxation, and decomposition techniques.

### Schedule

**Lecture**

### Lecturer(s)

### Prerequisites

Fundamentals in mathematics (including linear programming)

### Course Content

Many optimization problems in practice are nonlinear. This course introduces PhD students of information systems, business administration, and computer science to the fundamentals of nonlinear optimization theory and solution methods. The course is partly taught in a seminar-style format. Topics will be assigned in class based on student preferences and needs with regard to their thesis. Students will get a fundamental understanding of problems, theory and solution methods in nonlinear optimization. This includes to learn how to formulate a nonlinear optimization problem mathematically, how to analyze its structure to detect e.g. convexities, how to implement and solve a problem with state-of-the-art modeling environments and solvers. Students can bring in and work on their own problems of interest, e.g. a specific one that they might face in their thesis or an actual standard problem often encountered in practice.

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

his course aims to provide a working knowledge of basic probability theory and inductive statistics. The course is especially recommended for students wanting to refresh the skills required to attend the course Advanced Econometrics I (E703). The topics roughly align with appendices B, C, and D of the book Econometric Analysis by William H. Greene (2008, 6th ed.), for example: random variables, expectations, probability distributions, random sampling, point estimators, confidence intervals,, hypothesis testing, large sample distribution theory.

Background reading material:

- Greene, W. H., Econometric Analysis. Upper Saddle River: Pearson Prentice Hall, 2008.
- Introduction to Econometrics by Stock and Watson (2007, 2nd ed.), chapters 2 and 3.
- Introduction to Probability Models by Ross (2000, 2nd ed.), chapters 2.1-2.5, 2.7, and 3.1-3.4

Please note that the Statistics Refresher course will cover integrals and most of the basic statistics you’ll need in Advanced Econometrics I. These topics won’t be covered again in Advanced Econometrics I. Hence you are advised to attend the Statistics Refresher course, if you have some doubts about your knowledge regarding the above mentioned topics.

### Schedule

**Lecture**

### Prerequisites

basic mathematical knowledge

### Course Content

The course consists of four chapters:

**Requirements for the assignment of ECTS Credits and Grades:**

- Exam (120 min)

### Competences acquired

### Schedule

**Lecture**

**Tutorial**

### Prerequisites

E700

### Course Content

The course gives a foundation for studies for microeconomics at the PhD level. The first part is devoted to consumer and producer theory. It is organized as follows:

1. Choice, preference and utility

2. Structural properties of preferences and utility functions

3. Basics of consumer demand

4. Expenditure minimization

5. Classical demand theory

6. Competitive and profit-maximizing firms

7. Consumer and producer surplus

8. Choice under uncertainty

9. Utility for money

The second part covers game theory and is organized as follows:

10. Static games of complete information: Rationalizability and iterated strict dominance

11. Static games of complete information: Nash equilibrium

12. Static games of incomplete information

13. Dynamic games: The extensive form

14. Dynamic games: Equilibrium concepts

**Teaching method**

Lecture (3 SWS), Exercise (1.5 SWS)

**Requirements for the assignment of ECTS-Credits and Grades**

- Written exam: 120 min (90% weighting)
- Exercises (10% weighting)

**Literature**

Recommended textbooks:

- Fudenberg, D & Tirole, J. (1991). Game Theory. MIT Press
- Kreps, D. (2012). Microeconomic Foundation 1: Choice and Competitive Markets. Princeton University Press.
- Mas- Colell, A. Whinston, M.D. & Green, J. (1995). Microeconomic Theory. Oxford University Press.
- Osborne M. and Rubinstein, A. (1994): A Course in Game Theory. MIT Press

### Competences acquired

Students learn the basic tools for graduate level microeconomic analysis. The concepts learned in the course serve as building blocks for more advanced topics such as the ones studied in Advanced Microeconomics 2 and 3 and also for macroeconomics and empirical studies. Students also learn using rigorous formal proofs for microeconomic questions.

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Course Content

The course aims to provide the basic understanding of the institutions belonging to the nonprofit sector. Furthermore, the course addresses the relevant economic and managerial theories in order to be able to analyze the specific managerial problems of nonprofit organizations (NPOs).

Each student will be asked to work himself through a basic scientific ("classical") paper, enrich this paper by adding latest research results from currently published journal papers, and present the findings in class, where the results will be discussed.

Topics that may be touched include "History and Scope of the Nonprofit Sector",, "Nonprofits and the Marketplace", "Nonprofits and the Polity", "Key Activities in the Nonprofit Sector", and "Mission and Governance".

**Literature:**

Powell, W./Steinberg, R. (eds.) (2006): The Nonprofit Sector, 2nd edition, New Haven & London

Steinberg, R. (ed.) (2004): The Economics of Nonprofit Enterprises, Cheltenham, UK; Northampton, MA, USA

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

All researchers face similar challenges with core issues of research design. A research design is a plan that specifies how you are going to carry out a research project and, particularly, how to use evidence to answer your research question. The goal of this course is to jump-start students with their dissertation proposal. This course should help students to see the trade-offs involved in choosing a particular research design in their research projects. Consequently students are expected to develop own ideas about potential research questions and actively participate in those seminar-style meetings that are organized within this lecture course.

### Material

### Schedule

**Workshop**

### Lecturer(s)

### Course Content

Background reading material:

- Greene, W. H., Econometric Analysis. Upper Saddle River: Pearson Prentice Hall, 2008.
- Introduction to Econometrics by Stock and Watson (2007, 2nd ed.), chapters 2 and 3.
- Introduction to Probability Models by Ross (2000, 2nd ed.), chapters 2.1-2.5, 2.7, and 3.1-3.4

### Schedule

**Lecture**

### Lecturer(s)

### Prerequisites

### Course Content

The objective of the brown bag seminar is to use a lunch break to discuss research projects. The format is intended to offer PhD students and postdocs an opportunity to present and discuss their work to members from different chairs of the management area in an informal setting.

You can either participate actively as a presenter or join our group as an audience member. In each meeting, there will be either one research presentation or several idea development presentations.

If you would also like to present in the future brow bag seminars, please send a short notice to Katja Dlouhy (katja.dlouhy(at)bwl.uni-mannheim.de) with project title and suggested dates (Wednesdays, 12:00-13:30)

### Schedule

**Lecture**

### Prerequisites

basic mathematical knowledge

### Course Content

The course consists of four chapters:

**Requirements for the assignment of ECTS Credits and Grades:**

- Exam (120 min)

### Competences acquired

### Schedule

**Lecture**

**Tutorial**

### Prerequisites

E700

### Course Content

1. Choice, preference and utility

2. Structural properties of preferences and utility functions

3. Basics of consumer demand

4. Expenditure minimization

5. Classical demand theory

6. Competitive and profit-maximizing firms

7. Consumer and producer surplus

8. Choice under uncertainty

9. Utility for money

The second part covers game theory and is organized as follows:

10. Static games of complete information: Rationalizability and iterated strict dominance

11. Static games of complete information: Nash equilibrium

12. Static games of incomplete information

13. Dynamic games: The extensive form

14. Dynamic games: Equilibrium concepts

**Teaching method**

Lecture (3 SWS), Exercise (1.5 SWS)

**Requirements for the assignment of ECTS-Credits and Grades**

- Written exam: 120 min (90% weighting)
- Exercises (10% weighting)

**Literature**

Recommended textbooks:

- Fudenberg, D & Tirole, J. (1991). Game Theory. MIT Press
- Mas- Colell, A. Whinston, M.D. & Green, J. (1995). Microeconomic Theory. Oxford University Press.
- Osborne M. and Rubinstein, A. (1994): A Course in Game Theory. MIT Press

### Competences acquired

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Prerequisites

E700

The course is intended for Masters and first year PhD students with prior knowledge of undergraduate level econometrics. Working knowledge of basic probability theory, differential calculus, linear algebra and matrix algebra are assumed. Students should check if they are sufficiently familiar with these topics.

### Course Content

The course is designed to offer an advanced treatment to econometric theory and applications. Topics covered include: Repetition of ordinary least squares and generalized least squares, instrumental variables estimation, simultaneous equations, generalized method of moments and maximum likelihood estimation, time series and panel data econometrics. Attendance in the lectures and exercise sessions are mandatory. Attempting exercise questions ahead of each session and taking active part during the course of the sessions is essential.

Rooms for Thursday seminars:

08.10.15 – SO 133

15.10.15-12.11.15 – O 131

19.11.15-10.12.15 – SO 133

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Course Content

The primary objective of this course is to gain a detailed understanding and practical working knowledge of research design and methodology fundamentals in marketing. This understanding requires a fluency in the terminology of research, as well as an appreciation of basic research techniques and concepts drawn from such diverse fields as psychology and statistics. Secondary objectives include stimulating research creativity and critical thinking in the realm of research design and methodology, and introducing and integrating a wide variety of research techniques relating to design and methodology issues. In this course, a diversity of instructional approaches (e.g., lecture, in-depth analysis and discussion of assigned articles, student presentations, a term paper, an examination) will be used. The emphasis will be on the practical application of research in furthering marketing knowledge.

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

Background reading material:

- Greene, W. H., Econometric Analysis. Upper Saddle River: Pearson Prentice Hall, 2008.
- Introduction to Econometrics by Stock and Watson (2007, 2nd ed.), chapters 2 and 3.
- Introduction to Probability Models by Ross (2000, 2nd ed.), chapters 2.1-2.5, 2.7, and 3.1-3.4

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

The goal of the course is to provide Ph.D. students an introduction in and overview of state-of-theartmethods of quantitative modeling. For students who are interested in quantitative methods, thiscourse will provide you an overview and the basic understandings of up-to-date discrete choicemethods in business and marketing research. Researchers use these statistical methods toexamine the choices that consumers, households, firms, and other agents make. Each of the majormodels is covered: logit, generalized extreme value (including nested and cross-nested logits),probit, and mixed logit, plus a variety of specifications that build on these basics. Simulationassistedestimation procedures are investigated and compared, including maximum simulated likelihood,method of simulated moments, and method of simulated scores. The course will also coverprocedures for endogeneity and expectation-maximization algorithms. Participants will study will avariety of articles and case studies which demonstrate the application of such models to realbusiness phenomena.

The lectures on "Advanced Business Econometrics" cover the following topics:

- Properties of Discrete Choice Model
- Logit Model
- Numerical Maximization
- Nested Logit
- Probit Model
- Mixed Logit
- Conditional Distributions of Individual-level Parameters
- Endogeneity: BLP, Control functions, Latent instruments

### Schedule

**Lecture**

### Prerequisites

basic mathematical knowledge

### Course Content

The course consists of four chapters:

**Requirements for the assignment of ECTS Credits and Grades:**

- Exam (120 min)

### Competences acquired

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Course Content

This course is designed for doctoral students in information systems and operations/logistics. It provides a basic understanding of philosophy of science and its epistemological foundations. On the one hand, the course will focus on those concepts which derive knowledge from observation, induction, and refutation of facts. Furthermore, it also takes experiments as well as the new experimentalism into account in order to refer to those disciplines that focus on the evaluation of artifacts like prototypes and algorithms for example. Thus, the underlying epistemological foundations are of central interest to every doctoral students who studies the structure and behavior of information systems and operations/logistics phenomena. The course will be offered in an interactive style. All doctoral students have to offer at least one presentation and a documentation regarding a specific epistemological stance. Furthermore, participants have to discuss an article from literature in order to apply and reinforce the epistemological stance presented. Assignment of topics will be conducted by the lecturer.

Introductory literature:

- Chalmers, A.F.: What is this thing called science? 4th edition, Hill and Wang Publisher, 2013.

or

- Chalmers, A.F.: Wege der Wissenschaft: Einführung in die Wissenschaftstheorie (German Edition), Springer 2006

These two books represents a starting point. They will be the basis for the discussion sessions with the instructor.

Recommendable is also the following book for further studies:

- Curd, M.; Cover, J.A.: Philosophy of Science - the Central Issues, 2nd edition, Norton publishers, 2012

For session 6, the following sources are recommended:

- Von Bertalanffy, L., “The History and Status of General Systems Theory”, The Academy of Management Journal, Vol. 15, No. 4, 1972.
- Simon, H.A., “The sciences of the artificial”, MIT Press, Cambridge, 1996. [Special focus on chapter 5: “The Science of Design: Creating the Artificial”]

You are requested to retrieve additional literature which deepens this respective stances. This will also guide your seminar paper.

### Lecturer(s)

### Prerequisites

Recommended: Fundamentals in mathematics (including Linear Programming)

### Course Content

This course aims at PhD students in information systems, business administration, and computer science. It provides a basic understanding of optimization problems and methods. The course is taught in a seminar-style format. Allocation of topics will be done together in the class.

### Competences acquired

The course aims to introduce the students to fundamental linear and combinatorial optimization problems. They learn to formulate optimization models as mixed-integer linear programs, how to construct heuristics, and how to analyze the performance of heuristic algorithms. The students learn to deal with the complexity of real-world problems via aggregation, relaxation, and decomposition techniques.

### Schedule

**Lecture**

### Lecturer(s)

### Prerequisites

Fundamentals in mathematics (including linear programming)

### Course Content

Many optimization problems in practice are nonlinear. This course introduces PhD students of information systems, business administration, and computer science to the fundamentals of nonlinear optimization theory and solution methods. The course is partly taught in a seminar-style format. Topics will be assigned in class based on student preferences and needs with regard to their thesis. Students will get a fundamental understanding of problems, theory and solution methods in nonlinear optimization. This includes to learn how to formulate a nonlinear optimization problem mathematically, how to analyze its structure to detect e.g. convexities, how to implement and solve a problem with state-of-the-art modeling environments and solvers. Students can bring in and work on their own problems of interest, e.g. a specific one that they might face in their thesis or an actual standard problem often encountered in practice.

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

Background reading material:

- Greene, W. H., Econometric Analysis. Upper Saddle River: Pearson Prentice Hall, 2008.
- Introduction to Econometrics by Stock and Watson (2007, 2nd ed.), chapters 2 and 3.
- Introduction to Probability Models by Ross (2000, 2nd ed.), chapters 2.1-2.5, 2.7, and 3.1-3.4

### Schedule

**Lecture**

### Lecturer(s)

### Prerequisites

Prerequisites: OPM 901 – Research Seminar in Operations Management and Operations Research

### Course Content

Aim of module: This elective course aims at PhD students in information systems, business administration, and computer science. The course is taught in a seminar-style format.

Each student gives three presentation about one own research topics to discuss and sharpen the contribution of that work. The presentations are structured similar to papers in that field:

- Problem description, Model formulation, and contribution to scientific literature
- Solution Method
- Data analysis and numerical study

Students act as discussants to presentations of other students. At the end of the seminar students hand in a draft of the paper, which reflects the discussion to each single point.

Learning outcomes: Students will learn how to structure and discuss their own research results for a presentation and in a paper. They will become acquainted with acting as discussant for other topics. They will learn how to identify and sharpen the contributions of their own work. They learn how to present the analysis of data and how to design numerical studies.

### Prerequisites

basic mathematical knowledge

### Course Content

The course consists of four chapters:

**Requirements for the assignment of ECTS Credits and Grades:**

- Exam (120 min)

### Competences acquired

### Schedule

**Lecture**

**Tutorial**

### Prerequisites

E700

### Course Content

1. Choice, preference and utility

2. Structural properties of preferences and utility functions

3. Basics of consumer demand

4. Expenditure minimization

5. Classical demand theory

6. Competitive and profit-maximizing firms

7. Consumer and producer surplus

8. Choice under uncertainty

9. Utility for money

The second part covers game theory and is organized as follows:

10. Static games of complete information: Rationalizability and iterated strict dominance

11. Static games of complete information: Nash equilibrium

12. Static games of incomplete information

13. Dynamic games: The extensive form

14. Dynamic games: Equilibrium concepts

**Teaching method**

Lecture (3 SWS), Exercise (1.5 SWS)

**Requirements for the assignment of ECTS-Credits and Grades**

- Written exam: 120 min (90% weighting)
- Exercises (10% weighting)

**Literature**

Recommended textbooks:

- Fudenberg, D & Tirole, J. (1991). Game Theory. MIT Press
- Mas- Colell, A. Whinston, M.D. & Green, J. (1995). Microeconomic Theory. Oxford University Press.
- Osborne M. and Rubinstein, A. (1994): A Course in Game Theory. MIT Press

### Competences acquired

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Prerequisites

E700

The course is intended for Masters and first year PhD students with prior knowledge of undergraduate level econometrics. Working knowledge of basic probability theory, differential calculus, linear algebra and matrix algebra are assumed. Students should check if they are sufficiently familiar with these topics.

### Course Content

Rooms for Thursday seminars:

08.10.15 – SO 133

15.10.15-12.11.15 – O 131

19.11.15-10.12.15 – SO 133

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

Background reading material:

- Greene, W. H., Econometric Analysis. Upper Saddle River: Pearson Prentice Hall, 2008.
- Introduction to Econometrics by Stock and Watson (2007, 2nd ed.), chapters 2 and 3.
- Introduction to Probability Models by Ross (2000, 2nd ed.), chapters 2.1-2.5, 2.7, and 3.1-3.4

### Schedule

**Lecture**

### Course Content

This course aims at students in accounting and taxation. The course is taught in a seminar-style format. Students present their own research and discuss the presentations of other students. Students are introduced in writing referee reports to (drafts of) papers. Allocation of topics will be determined in class.

Students will learn how to present and discuss their own research results. They will become acquainted with acting as discussant for other topics. Additionally, they will learn how to write a referee report.

### Schedule

**Lecture**

### Lecturer(s)

### Prerequisites

The course is intended for PhD students with some prior knowledge of undergraduate level econometrics and statistics.

### Course Content

The course enables students to apply the econometric methods which are commonly used in economic research. Special attention is given to the interpretation of empirical results and understanding the potential caveats of different approaches. Topics covered include: Ordinary least squares, instrumental variables estimation, and panel data econometrics. Further topics may also be included according to demand by participants. Attendance in the lectures and exercise sessions are mandatory. Attempting exercise questions ahead of each session and taking active part during the course of the sessions is essential.

### Schedule

**Lecture**

### Lecturer(s)

### Prerequisites

Basic understanding of EU Law and Tax Law

### Course Content

European Union Law has an increasing impact on the taxation of private individuals as well as of companies doing business in Europe. While the European Union has no original tax authority its law has a major influence on national tax laws.

The course will start with an introduction into European Union Law. It will describe the nature of European Law and the European institutions. After that the course will cover the positive harmonisation of indirect taxes mainly by European directives. In a third part the course will focus on secondary law harmonising direct taxes in Europe, e.g. the Parent-Subsidiary Directive. In a last section the course deals with the importance of the fundamental freedoms for the taxation in Europe. A special focus will be put on the case law of the European Court of Justice.

### Schedule

**Lecture**