BE INSPIRED

"The CDSB core courses and especially the electives are very useful because they equip us with solid skills in our field of research as well as in related fields." Kirstin Becker, CDSB

Course Catalog

Fall 2015

Lecturer(s)


Course Type: core course

Course Number: ACC801

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
24.11.15
Tuesday
13:45
17:00
SO 422
01.12.15
Tuesday
13:45
18:45
08.12.15
Tuesday
13:45
18:45
26.02.16
Friday
10:15
11:45


Course Type: core course

Course Number: E700

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.15
28.09.15
Monday
10:15
11:45
L7, 3-5 - 001
08.09.15
29.09.15
Tuesday
10:15
11:45
L7, 3-5 - 001
09.09.15
30.09.15
Wednesday
10:15
11:45
A5 - C012
10.09.15
01.10.15
Thursday
10:15
11:45
A5 - C013
Tutorial
Exercise Group 1
07.09.15
28.09.15
Monday
13:45
15:15
L7, 3-5 - P044
Exercise Group 2
07.09.15
28.09.15
Monday
13:45
15:15
L9, 7 - 308
Exercise Group 3
07.09.15
28.09.15
Monday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
07.09.15
28.09.15
Monday
15:30
17:00
L9, 7 - 308
Exercise Group 1
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 1-2 - 002
Exercise Group 2
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 7 - 509
Exercise Group 3
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
09.09.15
30.09.15
Wednesday
13:45
13:45
L9, 1-2 - 002
Exercise Group 2
09.09.15
30.09.15
Wednesday
13:45
15:15
L9, 1-2 - 003
Exercise Group 3
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
10.09.15
01.10.15
Thursday
15:30
17:00
L9, 1-2 - 009
Exercise Group 2
10.09.15
01.10.15
Thursday
15:30
17:00
A5 - C013
Exercise Group 3
10.09.15
01.10.15
Thursday
17:15
18:45
L9, 1-2 - 002
Exercise Group 4
10.09.15
01.10.15
Thursday
17:15
18:45
A5 - C013


Course Type: core course

Course Number: E701

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.10.15
07.12.15
Monday
10:15
11:45
L7, 3-5 - 001
14.10.15
09.12.15
Wednesday
10:15
11:45
L7, 3-5 001
Tutorial
Exercise Group 1
12.10.15
07.12.15
Monday
12:00
13:30
L7, 3-5 - P044
Exercise Group 2
12.10.15
07.12.15
Monday
13:45
15:15
L7, 3-5 - P044

Lecturer(s)


Course Type: core course

Course Number: E703

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.15
08.12.15
Tuesday
10:15
11:45
L 9, 1-2 - room 409
08.10.15
10.12.15
Thursday
10:15
11:45
see section above
Tutorial
Stata Tutorial
08.10.15
10.12.15
Thursday
15:30
17:00
L 7, 3-5 - 257 pool room
09.10.15
11.12.15
Friday
13:45
15:15
L 9, 1-2 - room 009

Lecturer(s)


Course Type: elective course

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
Friday
10:15
18:45
O135
18.09.15
Friday
10:15
18:45
O135
25.09.15
Friday
10:15
18:45
O133
02.10.15
Friday
10:15
11:45
O129
02.10.15
Friday
12:00
18:45
O145


Course Type: elective course

Course Number: ACC/TAX911

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
09.09.15
09.12.15
Wednesday
13:45
17:00
O 048

Lecturer(s)


Course Type: elective course

Course Number: ACC/TAX916

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.15
08.12.15
Tuesday
13:45
15:15
L 9, 1-2 - room 009
17.09.15
10.12.15
Thursday
13:45
15:15
L 9, 1-2 - room 009


Course Type: core course

Course Number: E700

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.15
28.09.15
Monday
10:15
11:45
L7, 3-5 - 001
08.09.15
29.09.15
Tuesday
10:15
11:45
L7, 3-5 - 001
09.09.15
30.09.15
Wednesday
10:15
11:45
A5 - C012
10.09.15
01.10.15
Thursday
10:15
11:45
A5 - C013
Tutorial
Exercise Group 1
07.09.15
28.09.15
Monday
13:45
15:15
L7, 3-5 - P044
Exercise Group 2
07.09.15
28.09.15
Monday
13:45
15:15
L9, 7 - 308
Exercise Group 3
07.09.15
28.09.15
Monday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
07.09.15
28.09.15
Monday
15:30
17:00
L9, 7 - 308
Exercise Group 1
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 1-2 - 002
Exercise Group 2
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 7 - 509
Exercise Group 3
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
09.09.15
30.09.15
Wednesday
13:45
13:45
L9, 1-2 - 002
Exercise Group 2
09.09.15
30.09.15
Wednesday
13:45
15:15
L9, 1-2 - 003
Exercise Group 3
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
10.09.15
01.10.15
Thursday
15:30
17:00
L9, 1-2 - 009
Exercise Group 2
10.09.15
01.10.15
Thursday
15:30
17:00
A5 - C013
Exercise Group 3
10.09.15
01.10.15
Thursday
17:15
18:45
L9, 1-2 - 002
Exercise Group 4
10.09.15
01.10.15
Thursday
17:15
18:45
A5 - C013


Course Type: core course

Course Number: E701

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.10.15
07.12.15
Monday
10:15
11:45
L7, 3-5 - 001
14.10.15
09.12.15
Wednesday
10:15
11:45
L7, 3-5 001
Tutorial
Exercise Group 1
12.10.15
07.12.15
Monday
12:00
13:30
L7, 3-5 - P044
Exercise Group 2
12.10.15
07.12.15
Monday
13:45
15:15
L7, 3-5 - P044

Lecturer(s)


Course Type: core course

Course Number: E703

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.15
08.12.15
Tuesday
10:15
11:45
L 9, 1-2 - room 409
08.10.15
10.12.15
Thursday
10:15
11:45
see section above
Tutorial
Stata Tutorial
08.10.15
10.12.15
Thursday
15:30
17:00
L 7, 3-5 - 257 pool room
09.10.15
11.12.15
Friday
13:45
15:15
L 9, 1-2 - room 009

Lecturer(s)


Course Type: core course

Course Number: FIN801

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
29.09.15
01.12.15
Tuesday
15:30
19:00
L9, 7 room 308
02.12.15
Wednesday
17:30
19:00
L9, 7 room 308

Lecturer(s)


Course Type: elective course

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
Friday
10:15
18:45
O135
18.09.15
Friday
10:15
18:45
O135
25.09.15
Friday
10:15
18:45
O133
02.10.15
Friday
10:15
11:45
O129
02.10.15
Friday
12:00
18:45
O145

Lecturer(s)


Course Type: elective course

Course Number: FIN920

Credits: 4

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
14.10.15
Wednesday
14:00
16:00
Kaiserring 10–16, room 201/202
28.10.15
Wednesday
14:00
18:00
Kaiserring 10–16, room 201/202
18.11.15
Wednesday
14:00
18:00
Kaiserring 10–16, room 201/202
04.12.15
Friday
14:00
18:00
Kaiserring 10–16, room 201/202


Course Type: core course

Course Number: E700

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.15
28.09.15
Monday
10:15
11:45
L7, 3-5 - 001
08.09.15
29.09.15
Tuesday
10:15
11:45
L7, 3-5 - 001
09.09.15
30.09.15
Wednesday
10:15
11:45
A5 - C012
10.09.15
01.10.15
Thursday
10:15
11:45
A5 - C013
Tutorial
Exercise Group 1
07.09.15
28.09.15
Monday
13:45
15:15
L7, 3-5 - P044
Exercise Group 2
07.09.15
28.09.15
Monday
13:45
15:15
L9, 7 - 308
Exercise Group 3
07.09.15
28.09.15
Monday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
07.09.15
28.09.15
Monday
15:30
17:00
L9, 7 - 308
Exercise Group 1
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 1-2 - 002
Exercise Group 2
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 7 - 509
Exercise Group 3
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
09.09.15
30.09.15
Wednesday
13:45
13:45
L9, 1-2 - 002
Exercise Group 2
09.09.15
30.09.15
Wednesday
13:45
15:15
L9, 1-2 - 003
Exercise Group 3
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
10.09.15
01.10.15
Thursday
15:30
17:00
L9, 1-2 - 009
Exercise Group 2
10.09.15
01.10.15
Thursday
15:30
17:00
A5 - C013
Exercise Group 3
10.09.15
01.10.15
Thursday
17:15
18:45
L9, 1-2 - 002
Exercise Group 4
10.09.15
01.10.15
Thursday
17:15
18:45
A5 - C013

Lecturer(s)


Course Type: core course

Course Number: IS901

Credits: 8

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)


Course Type: core course

Course Number: OPM801

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
21.10.15
09.12.15
Wednesday
15:30
18:45
SO 318

Lecturer(s)


Course Type: core course

Course Number: OPM803

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
09.10.15
11.12.15
Friday
10:15
13:30
SO 318

Lecturer(s)


Course Type: elective course

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
Friday
10:15
18:45
O135
18.09.15
Friday
10:15
18:45
O135
25.09.15
Friday
10:15
18:45
O133
02.10.15
Friday
10:15
11:45
O129
02.10.15
Friday
12:00
18:45
O145


Course Type: core course

Course Number: E700

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.15
28.09.15
Monday
10:15
11:45
L7, 3-5 - 001
08.09.15
29.09.15
Tuesday
10:15
11:45
L7, 3-5 - 001
09.09.15
30.09.15
Wednesday
10:15
11:45
A5 - C012
10.09.15
01.10.15
Thursday
10:15
11:45
A5 - C013
Tutorial
Exercise Group 1
07.09.15
28.09.15
Monday
13:45
15:15
L7, 3-5 - P044
Exercise Group 2
07.09.15
28.09.15
Monday
13:45
15:15
L9, 7 - 308
Exercise Group 3
07.09.15
28.09.15
Monday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
07.09.15
28.09.15
Monday
15:30
17:00
L9, 7 - 308
Exercise Group 1
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 1-2 - 002
Exercise Group 2
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 7 - 509
Exercise Group 3
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
09.09.15
30.09.15
Wednesday
13:45
13:45
L9, 1-2 - 002
Exercise Group 2
09.09.15
30.09.15
Wednesday
13:45
15:15
L9, 1-2 - 003
Exercise Group 3
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
10.09.15
01.10.15
Thursday
15:30
17:00
L9, 1-2 - 009
Exercise Group 2
10.09.15
01.10.15
Thursday
15:30
17:00
A5 - C013
Exercise Group 3
10.09.15
01.10.15
Thursday
17:15
18:45
L9, 1-2 - 002
Exercise Group 4
10.09.15
01.10.15
Thursday
17:15
18:45
A5 - C013


Course Type: core course

Course Number: E701

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.10.15
07.12.15
Monday
10:15
11:45
L7, 3-5 - 001
14.10.15
09.12.15
Wednesday
10:15
11:45
L7, 3-5 001
Tutorial
Exercise Group 1
12.10.15
07.12.15
Monday
12:00
13:30
L7, 3-5 - P044
Exercise Group 2
12.10.15
07.12.15
Monday
13:45
15:15
L7, 3-5 - P044

Lecturer(s)


Course Type: core course

Course Number: MAN802

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
18.09.15
Friday
10:00
12:00
Q&A-Session
16.10.15
Friday
10:00
12:00
L 5,4 - room 207/209
Presentation Session
20.11.15
Friday
09:00
17:00
L 5,4 - room 207/209

Lecturer(s)


Course Type: core course

Course Number: MET

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Workshop
07.09.15
07.12.15
Mondays
13:45
15:30
D7,27, room 307

Lecturer(s)


Course Type: elective course

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
Friday
10:15
18:45
O135
18.09.15
Friday
10:15
18:45
O135
25.09.15
Friday
10:15
18:45
O133
02.10.15
Friday
10:15
11:45
O129
02.10.15
Friday
12:00
18:45
O145

Lecturer(s)


Course Type: elective course

Course Number: MAN911

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
23.09.15
09.12.15
Wednesday
12:00
13:30
L 9, 1-2, room 409
11.11.15
Wednesday
12:00
13:30
O 151


Course Type: core course

Course Number: E700

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.15
28.09.15
Monday
10:15
11:45
L7, 3-5 - 001
08.09.15
29.09.15
Tuesday
10:15
11:45
L7, 3-5 - 001
09.09.15
30.09.15
Wednesday
10:15
11:45
A5 - C012
10.09.15
01.10.15
Thursday
10:15
11:45
A5 - C013
Tutorial
Exercise Group 1
07.09.15
28.09.15
Monday
13:45
15:15
L7, 3-5 - P044
Exercise Group 2
07.09.15
28.09.15
Monday
13:45
15:15
L9, 7 - 308
Exercise Group 3
07.09.15
28.09.15
Monday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
07.09.15
28.09.15
Monday
15:30
17:00
L9, 7 - 308
Exercise Group 1
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 1-2 - 002
Exercise Group 2
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 7 - 509
Exercise Group 3
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
09.09.15
30.09.15
Wednesday
13:45
13:45
L9, 1-2 - 002
Exercise Group 2
09.09.15
30.09.15
Wednesday
13:45
15:15
L9, 1-2 - 003
Exercise Group 3
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
10.09.15
01.10.15
Thursday
15:30
17:00
L9, 1-2 - 009
Exercise Group 2
10.09.15
01.10.15
Thursday
15:30
17:00
A5 - C013
Exercise Group 3
10.09.15
01.10.15
Thursday
17:15
18:45
L9, 1-2 - 002
Exercise Group 4
10.09.15
01.10.15
Thursday
17:15
18:45
A5 - C013


Course Type: core course

Course Number: E701

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.10.15
07.12.15
Monday
10:15
11:45
L7, 3-5 - 001
14.10.15
09.12.15
Wednesday
10:15
11:45
L7, 3-5 001
Tutorial
Exercise Group 1
12.10.15
07.12.15
Monday
12:00
13:30
L7, 3-5 - P044
Exercise Group 2
12.10.15
07.12.15
Monday
13:45
15:15
L7, 3-5 - P044

Lecturer(s)


Course Type: core course

Course Number: E703

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.15
08.12.15
Tuesday
10:15
11:45
L 9, 1-2 - room 409
08.10.15
10.12.15
Thursday
10:15
11:45
see section above
Tutorial
Stata Tutorial
08.10.15
10.12.15
Thursday
15:30
17:00
L 7, 3-5 - 257 pool room
09.10.15
11.12.15
Friday
13:45
15:15
L 9, 1-2 - room 009

Lecturer(s)


Course Type: core course

Course Number: MKT801

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
04.12.15
Friday
10:15
11:45
L9, 1-2, room 009

Lecturer(s)


Course Type: elective course

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
Friday
10:15
18:45
O135
18.09.15
Friday
10:15
18:45
O135
25.09.15
Friday
10:15
18:45
O133
02.10.15
Friday
10:15
11:45
O129
02.10.15
Friday
12:00
18:45
O145

Lecturer(s)


Course Type: elective course

Course Number: MKT911

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.10.15
Thursday
09:30
17:00
L 5, 1 room 009
30.10.15
Friday
09:30
17:00
L 5, 1 room 009
20.11.15
Friday
09:30
17:00
L 5, 1 room 009
04.12.15
Friday
09:30
17:00
L 5, 1 room 009


Course Type: core course

Course Number: E700

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.15
28.09.15
Monday
10:15
11:45
L7, 3-5 - 001
08.09.15
29.09.15
Tuesday
10:15
11:45
L7, 3-5 - 001
09.09.15
30.09.15
Wednesday
10:15
11:45
A5 - C012
10.09.15
01.10.15
Thursday
10:15
11:45
A5 - C013
Tutorial
Exercise Group 1
07.09.15
28.09.15
Monday
13:45
15:15
L7, 3-5 - P044
Exercise Group 2
07.09.15
28.09.15
Monday
13:45
15:15
L9, 7 - 308
Exercise Group 3
07.09.15
28.09.15
Monday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
07.09.15
28.09.15
Monday
15:30
17:00
L9, 7 - 308
Exercise Group 1
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 1-2 - 002
Exercise Group 2
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 7 - 509
Exercise Group 3
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
09.09.15
30.09.15
Wednesday
13:45
13:45
L9, 1-2 - 002
Exercise Group 2
09.09.15
30.09.15
Wednesday
13:45
15:15
L9, 1-2 - 003
Exercise Group 3
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
10.09.15
01.10.15
Thursday
15:30
17:00
L9, 1-2 - 009
Exercise Group 2
10.09.15
01.10.15
Thursday
15:30
17:00
A5 - C013
Exercise Group 3
10.09.15
01.10.15
Thursday
17:15
18:45
L9, 1-2 - 002
Exercise Group 4
10.09.15
01.10.15
Thursday
17:15
18:45
A5 - C013

Lecturer(s)


Course Type: core course

Course Number: IS901

Credits: 8

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)


Course Type: core course

Course Number: OPM801

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
21.10.15
09.12.15
Wednesday
15:30
18:45
SO 318

Lecturer(s)


Course Type: core course

Course Number: OPM803

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
09.10.15
11.12.15
Friday
10:15
13:30
SO 318

Lecturer(s)


Course Type: elective course

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
Friday
10:15
18:45
O135
18.09.15
Friday
10:15
18:45
O135
25.09.15
Friday
10:15
18:45
O133
02.10.15
Friday
10:15
11:45
O129
02.10.15
Friday
12:00
18:45
O145

Lecturer(s)


Course Type: elective course

Course Number: OPM918

Credits: 8

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:

 

  1. Problem description, Model formulation, and contribution to scientific literature
  2. Solution Method
  3. 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.




Course Type: core course

Course Number: E700

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.15
28.09.15
Monday
10:15
11:45
L7, 3-5 - 001
08.09.15
29.09.15
Tuesday
10:15
11:45
L7, 3-5 - 001
09.09.15
30.09.15
Wednesday
10:15
11:45
A5 - C012
10.09.15
01.10.15
Thursday
10:15
11:45
A5 - C013
Tutorial
Exercise Group 1
07.09.15
28.09.15
Monday
13:45
15:15
L7, 3-5 - P044
Exercise Group 2
07.09.15
28.09.15
Monday
13:45
15:15
L9, 7 - 308
Exercise Group 3
07.09.15
28.09.15
Monday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
07.09.15
28.09.15
Monday
15:30
17:00
L9, 7 - 308
Exercise Group 1
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 1-2 - 002
Exercise Group 2
08.09.15
29.09.15
Tuesday
13:45
15:15
L9, 7 - 509
Exercise Group 3
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
08.09.15
29.09.15
Tuesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
09.09.15
30.09.15
Wednesday
13:45
13:45
L9, 1-2 - 002
Exercise Group 2
09.09.15
30.09.15
Wednesday
13:45
15:15
L9, 1-2 - 003
Exercise Group 3
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 1-2 - 002
Exercise Group 4
09.09.15
30.09.15
Wednesday
15:30
17:00
L9, 7 - 509
Exercise Group 1
10.09.15
01.10.15
Thursday
15:30
17:00
L9, 1-2 - 009
Exercise Group 2
10.09.15
01.10.15
Thursday
15:30
17:00
A5 - C013
Exercise Group 3
10.09.15
01.10.15
Thursday
17:15
18:45
L9, 1-2 - 002
Exercise Group 4
10.09.15
01.10.15
Thursday
17:15
18:45
A5 - C013


Course Type: core course

Course Number: E701

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.10.15
07.12.15
Monday
10:15
11:45
L7, 3-5 - 001
14.10.15
09.12.15
Wednesday
10:15
11:45
L7, 3-5 001
Tutorial
Exercise Group 1
12.10.15
07.12.15
Monday
12:00
13:30
L7, 3-5 - P044
Exercise Group 2
12.10.15
07.12.15
Monday
13:45
15:15
L7, 3-5 - P044

Lecturer(s)


Course Type: core course

Course Number: E703

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.15
08.12.15
Tuesday
10:15
11:45
L 9, 1-2 - room 409
08.10.15
10.12.15
Thursday
10:15
11:45
see section above
Tutorial
Stata Tutorial
08.10.15
10.12.15
Thursday
15:30
17:00
L 7, 3-5 - 257 pool room
09.10.15
11.12.15
Friday
13:45
15:15
L 9, 1-2 - room 009

Lecturer(s)


Course Type: core course

Course Number: TAX902

Credits: 7

Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.01.16
29.01.16
Friday
13:45
17:15
SO 133

Lecturer(s)


Course Type: elective course

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.15
Friday
10:15
18:45
O135
18.09.15
Friday
10:15
18:45
O135
25.09.15
Friday
10:15
18:45
O133
02.10.15
Friday
10:15
11:45
O129
02.10.15
Friday
12:00
18:45
O145


Course Type: elective course

Course Number: ACC/TAX911

Credits: 6

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
09.09.15
09.12.15
Wednesday
13:45
17:00
O 048

Lecturer(s)


Course Type: elective course

Course Number: ACC/TAX916

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.15
08.12.15
Tuesday
13:45
15:15
L 9, 1-2 - room 009
17.09.15
10.12.15
Thursday
13:45
15:15
L 9, 1-2 - room 009

Lecturer(s)


Course Type: elective course

Course Number: TAX912

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
10.09.15
10.12.15
Thursday
12:00
13:30
A 3 - room 016

Register

Business Fall 2015

ACC801
Applied Methods & Tools in Accounting & Finance
E700
Mathematics for Economists
E701
Advanced Microeconomics I
E703
Advanced Econometrics I
Statistics Refresher
ACC/TAX911
Brown Bag Seminar Empirical Accounting & Taxation
ACC/TAX916
Applied Econometrics
FIN801
Discrete-Time Finance
FIN920
Topics in Macro-Finance
IS901
Epistemological Foundations of Information Systems and Operations
OPM801
Optimization and Heuristics
OPM803
Selected Topics in Nonlinear Optimization
MAN802
Fundamentals in Nonprofit Management Science
MET
Crafting Social Science Research
MAN911
Brown Bag Seminar
MKT801
Fundamentals of Marketing Research
MKT911
Advanced Business Econometrics
OPM918
Business Analytics
TAX902
Public Economics
TAX912
European Tax Law