# Course Catalog

## Fall 2014

### Lecturer(s)

### Course Content

This course is designed to introduce and guide Ph.D. students in the usage of methods and tools in empirical research in accounting and finance (A&F), and thus bring them quickly to the level at which they can "technically" implement empirical research ideas. Key topics include:

- Alternative data sources for empirical projects
- Databases in A&F
- Conceptual issues and problems in common databases in A&F
- Introduction to statistical software packets (SAS, STATA, STAT Transfer), and guidance on other resources available to master more complex research methods
- Example SAS and STATA code for the replication of an empirical paper published in a top tier "A+" academic accounting journal (e.g. Journal of Accounting & Economics, Journal of Accounting Research)
- Discussion on the publication process of empirical research (Academic integrity in empirical research; The review process, and correspondence with referees and editors; Guidance and exercise on how to write a referee report)
- Discussion of students semester replication projects

Please note: The kick-off meeting for the HWS 2014 course will be on

Wednesday, September 10, 2014, at 2pm in room SN 288.

For planning purposes, please register in advance with Christoph Sextroh (csextroh[at]mail.uni-mannheim.de) until September 8, 2014.

### Schedule

**Lecture**

### Course Content

This course is designed to be a primer on paradigms of advanced research in accounting. Its aim is to make the students familiar with the relevant state-of-the-art research methodologies in accounting. Therefore, a broad range of heterogeneous approaches will be covered that employ analytical, empirical, normative and experimental research methodologies and reflect the diversity of accounting research. Each approach will be illustrated with a discussion of currently explored research questions.

### 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**

### 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

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

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

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. A refresher course in statistics is offered from on the following dates: 05.09. (13:00-18:45, O 135), 19.09. (10:15-18:45, O 135), 26.09. (10:15-18:45, O 135), 10.10. (13:00-18:45, O 135).

### 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.

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Course Content

This 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)

### Prerequisites

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

A refresher course in statistics is offered from 10 o’clock to 18 o’clock on following dates: 06.09. (SO 133), 13.09. (O 129), 20.09. (O 129).

### 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.

Textbook:

Stock, J. H. and M. Watson, Introduction to Econometrics, 3rd ed., Amsterdam: Addison-Wesley Longman, 2011.

Complementary textbooks:

Angrist, J.D. and J.-S. Pischke, Mostly Harmless Econometrics, Princeton: Princeton Press, 2009.

Other reading materials:

Hayashi, F., Econometrics. Princeton: Princeton University Press, 2000.

Verbeek, M., A Guide to Modern Econometrics. Chichester: John Wiley & Sons, 2008.

Hamilton, J. D., Time Series Analysis. Princeton: Princeton University Press, 1994.

Greene, W. H., Econometric Analysis. 7th ed., Upper Saddle River: Pearson Prentice Hall, 2011.

Wooldridge, J., Econometric Analysis of Cross Section and Panel Data. 2nd ed. Cambridge: MIT Press, 2010.

### 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**

### 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

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

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

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. A refresher course in statistics is offered from on the following dates: 05.09. (13:00-18:45, O 135), 19.09. (10:15-18:45, O 135), 26.09. (10:15-18:45, O 135), 10.10. (13:00-18:45, O 135).

### 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.

### Schedule

**Lecture**

**Tutorial**

### Lecturer(s)

### Prerequisites

E700 Mathematics for Economists

### Course Content

The purpose of this course is to introduce students to asset pricing and portfolio choice. Using the concepts of no-arbitrage and equilibrium, we will discuss decision-making under uncertainty and the existence of „state prices“ (discount factors such that the price of any security equals its discounted expected payoff). In the first part of the course, we will consider static settings which we extend to the multi-period framework in the second part. The final part of the course will be devoted to discussing the concept of asymmetric information and its implications for the equilbirum concept.

Topics covered:

- Portfolio theory
- Utility theory and decision under uncertainy
- Mean-variance analysis
- Consumption-based security pricing
- Complete and incomplete security markets
- Asset pricing with asymmetric information
- Multidate security markets

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

- Term paper and paper presentation 50%
- Final exam 50%

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

This 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 focuses on recent research topics in finance. In the course, we will discuss the papers presented by the seminar speakers in the University of Mannheim Finance faculty seminar. The class will typically take place on those Mondays during the semester on which a seminar presentation by an external speaker will be given.

For updates on the schedule, please regularly consult the following webpage:

www.finance.uni-mannheim.de

The format of the course consists of two main parts. In the morning of seminar days we will meet and one student will present the paper that will be presented in the afternoon in the official faculty seminar by an external speaker. The presentation should be about 30 minutes. Another student will then discuss the paper (like a formal extended conference discussion, max 15-20min). Based on that, we will then discuss the paper and its contribution to the literature in the forum. Thus, each student is required to carefully read all papers prior to class. Each student will present and discuss at least once during the semester. Topics will be assigned during the introductory meeting. Furthermore, each participant is required to write a short (1-2 pages max) referee report on one additional paper that he or she is not discussing or presenting. The second part is an informal meeting with the speaker prior to the seminar (if the speaker’s schedule allows). In this meeting, neither I nor other senior faculty members will be present and students are free to talk about the paper or whatever other topic that is relevant for finance researchers and in which students and the speaker share a common interest (“Cookies with the Speaker”). Regular participation in the morning sessions as well as in the meetings with the speakers are a necessary condition to fulfill the course requirements.

Course Materials:

The course is based on the papers presented during the faculty seminar. The respective papers will be posted on the seminar webpage.

Note: I reserve the right to make modification to this syllabus. The modifications (if any) will be announced in class. You are responsible for all announcements made in class.

### Competences acquired

During this course, students learn to understand and discuss research topics (potentially including topics from fields in which they might not be experts). This will allow them to profit better from the official seminar presentations and develop new research ideas themselves. Furthermore, they will learn how to develop and structure a discussion of a research paper. This will be useful for future conference participations (as you are probably aware of, presenters at conferences are typically asked to discuss another presenter’s paper, too). Finally, the meetings with the speaker will give students the possibility to speak to the presenters in an informal atmosphere and discuss their own or the speaker’s research or talk about other issues like career development, exchange visits, or the international job market process.

Students are required to participate in the morning classes and the “Cookies with Speaker” sessions. Regular participation is necessary to fulfill the course requirements. If you cannot come to the meetings for some justified reason, you have to let me know in advance. Additionally, if you miss meetings on more than two days, you will not pass the course. While not part of this course, participation in the faculty seminar is of course also mandatory for all finance PhD students. Your active participation is encouraged.

### Schedule

**Lecture**

### Lecturer(s)

### Course Content

In this course, students will become familiar with a wide range of market microstructure models and gain the skills to empirically calibrate and test these models. At the core of this course is the relation between liquidity, market structure and market design, the price process, and price discovery. In the first part of the course, we will focus on order flow, liquidity, and price dynamics. We will then discuss different market types, such as limit order markets and decentralized search markets. The third part of the course will be concerned with the relation between liquidity and asset prices. Time permitting, we will also cover recent market developments including algorithmic and high-frequency trading and the impact of market fragmentation on market quality.

Topics covered:

- Liquidity demand and supply
- Measuring liquidity
- Order flow, order imbalances, and price impact
- Asymmetric information and market quality
- Limit order trading
- Funding liquidity
- Market liquidity

### 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**

### 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? 3rd edition, Open University Press, Maiden-head 1999.

This book only represents a starting point. Literature for the assignments has to be retrieved by participants.

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.
- Weber, R., “Toward a theory of artifacts: A paradigmatic base for information systems re-search”, Journal of Information Systems, Vol. 1, No. 2, 1987.
- Simon, H.A., “The sciences of the artificial”, MIT Press, Cambridge, 1996. [Special focus on chapter 5: “The Science of Design: Creating the Artificial”]

### Schedule

**Lecture**

### 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

This 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)

### Prerequisites

Recommended: Fundamentals in operating systems or a general systems course

### Course Content

This course will discuss and emphasize Operating Systems principles. The overall goal is to apply the concepts and build a small operating system. Thus, the major learning outcome is the practical implementation of concepts. We will focus on the essential core concepts: process realization and synchronization. The implementation will be done in a virtual machine but with a realistic bootstrap.

A prerequisite of the course is an operating system or general systems course. We will partly review the concepts based on Tanenbaum's Modern Operating Systems text book.

Registration: Please contact Prof. Becker via email and send an email to GESS registration.

Literature: Andrew S. Tanenbaum: Modern Operating Systems. Prentice Hall, 3rd edition, 2007.

### 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**

### 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

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

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

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**

### Prerequisites

Content of MAN610 or MAN672

### 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 will 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'.

To participate in this course please register by sending an e-mail to registration@gess.uni-mannheim.de

Within this mail please provide the following information:

Your full name

Your matriculation number

Which center of the GESS you belong to (e.g. CDSB)

If it applies, the name of your mentor (the professor you are assigned to)

Your research interests and topics

Relevant documents can be found in the according E-learning group on ILIAS (sign up via: portal.uni-mannheim.de)

### Schedule

**Lecture**

### 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**

### 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**

### Prerequisites

E700.

### Course Content

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

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

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

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. A refresher course in statistics is offered from on the following dates: 05.09. (13:00-18:45, O 135), 19.09. (10:15-18:45, O 135), 26.09. (10:15-18:45, O 135), 10.10. (13:00-18:45, O 135).

### 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.

### 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**

### 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

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**Lecture**

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### 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? 3rd edition, Open University Press, Maiden-head 1999.

This book only represents a starting point. Literature for the assignments has to be retrieved by participants.

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.
- Weber, R., “Toward a theory of artifacts: A paradigmatic base for information systems re-search”, Journal of Information Systems, Vol. 1, No. 2, 1987.
- Simon, H.A., “The sciences of the artificial”, MIT Press, Cambridge, 1996. [Special focus on chapter 5: “The Science of Design: Creating the Artificial”]

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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.

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### 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.

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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

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### Prerequisites

Recommended: Fundamentals in operating systems or a general systems course

### Course Content

This course will discuss and emphasize Operating Systems principles. The overall goal is to apply the concepts and build a small operating system. Thus, the major learning outcome is the practical implementation of concepts. We will focus on the essential core concepts: process realization and synchronization. The implementation will be done in a virtual machine but with a realistic bootstrap.

A prerequisite of the course is an operating system or general systems course. We will partly review the concepts based on Tanenbaum's Modern Operating Systems text book.

Registration: Please contact Prof. Becker via email and send an email to GESS registration.

Literature: Andrew S. Tanenbaum: Modern Operating Systems. Prentice Hall, 3rd edition, 2007.

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**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**

### Prerequisites

E700.

### Course Content

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

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

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

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. A refresher course in statistics is offered from on the following dates: 05.09. (13:00-18:45, O 135), 19.09. (10:15-18:45, O 135), 26.09. (10:15-18:45, O 135), 10.10. (13:00-18:45, O 135).

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### Schedule

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**Tutorial**

### Course Content

This course is designed to be a primer on paradigms of advanced research in taxation with the aim to make students familiar with the relevant state-of-the-art research methodologies. The main focus is on empirical research methods which are illustrated by presentations of current projects. Additionally, some sessions are dedicated to research in accounting to understand the relation between the two fields.

### 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

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

A refresher course in statistics is offered from 10 o’clock to 18 o’clock on following dates: 06.09. (SO 133), 13.09. (O 129), 20.09. (O 129).

### 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.

Textbook:

Stock, J. H. and M. Watson, Introduction to Econometrics, 3rd ed., Amsterdam: Addison-Wesley Longman, 2011.

Complementary textbooks:

Angrist, J.D. and J.-S. Pischke, Mostly Harmless Econometrics, Princeton: Princeton Press, 2009.

Other reading materials:

Hayashi, F., Econometrics. Princeton: Princeton University Press, 2000.

Verbeek, M., A Guide to Modern Econometrics. Chichester: John Wiley & Sons, 2008.

Hamilton, J. D., Time Series Analysis. Princeton: Princeton University Press, 1994.

Greene, W. H., Econometric Analysis. 7th ed., Upper Saddle River: Pearson Prentice Hall, 2011.

Wooldridge, J., Econometric Analysis of Cross Section and Panel Data. 2nd ed. Cambridge: MIT Press, 2010.

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**Lecture**

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### 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.

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**Lecture**