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 2017

Lecturer(s)


Course Type: core course

Course Number: ACC 902

Credits: 8

Course Content

This course investigates strategies of normative research with regard to International Financial Reporting Standards (IFRS) from an interdisciplinary perspective. In the first part of the course, we discuss the foundations of normative accounting research. In particular, we show how this research methodology can successfully be applied despite claims of its alleged impossibility. Furthermore, we compare the legal traditions of normative interpretation in the US and in Europe. In the second part, we analyze the existing system of IFRS from different conceptual approaches and develop grounds for their further general development as well as solutions.

Learning outcomes: The purpose is to equip PhD students with the ability to do normative research in financial accounting, thus, to reason qualitatively on topical issues in the field of global financial reporting. The focus is on investigating strategies of normative research with regard to International Financial Reporting Standards (IFRS) from an interdisciplinary perspective, comprising fields as legal theory and other social sciences.

Form of assessment: Paper, Presentation


Please note: Only Kick-off and Assessment dates are available. All other course dates will be announced during the Kick-off session.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.09.17
Wednesday
13:45
15:15
O 254
06.12.17
Wednesday
14:00
18:00
O 254

Lecturer(s)


Course Type: core course

Course Number: ACC 911

Credits: 8

Prerequisites

Formal: Positively evaluated dissertation proposal and first-year.

Recommended: ACC 903 and ACC 904


Course Content

Building on the content of their dissertation proposal over the summer, the students further develop their idea into a first-draft working paper over the semester and course. Guided by participation and presentation in the Area Brown Bag seminar, project meetings with faculty, and by incorporating feedback received throughout the process, students prepare their first research paper. Their research skills are further developed by a referee report assignment of a working paper (e.g.,  presented in the Area Brown Bag,  Research Seminars, or submission).


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.10.17
Wednesday
13:45
15:15
SO 133
04.10.17
Wednesday
15:30
17:00
O 261
25.10.17
Wednesday
15:30
17:00
O 261
22.11.17
Wednesday
17:00
18:30
O 261
06.12.17
Wednesday
13:45
15:15
SO 133
06.12.17
Wednesday
15:30
17:00
O 261

Lecturer(s)


Course Type: core course

Course Number: E 703

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.17
Friday
08:30
18:45
L9, 7 – room 308
10.10.17
12.12.17
Tuesday
10:15
11:45
L9, 1-2 – room 002
12.10.17
14.12.17
Thursday
10:15
11:45
L9, 1-2 – room 409
Tutorial
11.10.17
13.12.17
Wednesday
12:00
13:30
L7, 3-5, room 257
13.10.17
15.12.17
Friday
13:45
15:15
SO 133


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.

 

For more detailed information about the first sessions of this course, click here.

 

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.

 

Teaching Assistants:

Daria Khromenkova (CDSE)

Exercise Groups 1 + 2

 

Sebastian Merkel (CDSE)

Exercise Groups 3 + 4


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.17
Monday
10:15
11:45
Room C012, Building A5, 6
05.09.17
Tuesday
10:15
11:45
Room C012, Building A5, 6
06.09.17
Wednesday
10:15
11:45
Room C012, Building A5, 6
07.09.17
Thursday
10:15
11:45
Room C012, Building A5, 6
Final Exam Date
05.10.17
Thursday
17:15
19:15
Room C013, Building A5, 6
Retake
14.12.17
Thursday
17:15
19:15
C012 in A5,6 Bauteil C
Tutorial
Exercise Group 1
04.09.17
25.09.17
Monday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
04.09.17
25.09.17
Monday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
04.09.17
25.09.17
Monday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
04.09.17
25.09.17
Monday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
05.09.17
26.09.17
Tuesday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
05.09.17
26.09.17
Tuesday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
05.09.17
26.09.17
Tuesday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
05.09.17
26.09.17
Tuesday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
06.09.17
27.09.17
Wednesday
13:45
15:15
Room A301, Building B6, 23-25
Exercise Group 3
06.09.17
27.09.17
Wednesday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
06.09.17
27.09.17
Wednesday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
06.09.17
27.09.17
Wednesday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
07.09.17
28.09.17
Thursday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
07.09.17
28.09.17
Thursday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
07.09.17
28.09.17
Thursday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
07.09.17
28.09.17
Thursday
15:30
17:00
Room A303, Building B6, 23-25

Lecturer(s)


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.

 

Teaching Assistant:

Larionov


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
09.10.17
06.12.17
Monday
10:15
11:45
Room 001, Building L7, 3-5 (tbc)
09.10.17
06.12.17
Wednesday
10:15
11:45
Room 001, Building L7, 3-5 (tbc)
Single Date
03.11.17
Friday
12:00
13:30
SO 318, Schneckenhof Ost
Written Exam
20.12.17
WED
08:30
10:30
L7, 3-5, 001
Retake
29.01.18
Monday
10:15
12:15
room 212/213, B6, 30-32
Tutorial
Exercise Group 1
09.10.17
06.12.17
Monday
12:00
13:30
Room 001, Building L7, 3-5
Exercise Group 2
09.10.17
06.12.17
Monday
13:45
15:15
Room 001, Building L7, 3-5

Lecturer(s)


Course Type: elective course

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
  • http://theanalysisofdata.com/probability/0_1.html

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
08.09.17
22.09.17
Friday
08:30
18:45
O 048
13.10.17
Friday
15:30
18:45
L9, 7 – room 308
20.10.17
Friday
08:30
18:45
L9, 7 - room 308

Lecturer(s)


Course Type: elective course

Course Number: ACC 905

Credits: 8

Prerequisites

 


Course Content

Based on an overview of current trends in empirical accounting research, the publication process, and typical project workflows, the course introduces relevant data sources and gives an introduction to empirical research using the statistical software STATA. The core part of the course consists of a group assignment that requires the replication of a high quality research paper in accounting, finance or tax research.

Learning outcomes: Know how to plan an empirical project in our field of research, how to execute an empirical analysis in STATA and learn the basics about selecting an appropriate outlet and getting through the publication process. The course is designed to prepare students to efficiently execute their own empirical research ideas in our field going forward.

Form of assessment: Oral exam (30 minutes), 25 %, Presentation 75 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.17
23.10.17
Monday
14:00
17:00
SO 133
07.09.17
26.10.17
Thursday
14:00
17:00
SO 133

Lecturer(s)


Course Type: elective course

Course Number: ACC 906

Credits: 8

Prerequisites

ACC 802

Recommended: Agency-theory, Decision theory and Game theory


Course Content

Selected accounting issues presented in a seminar-like form.

Learning outcomes: Students develop and present their own model dealing with a recent accounting or auditing issue.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.09.17
05.12.17
Tuesday
10:15
11:45
O 048


Course Type: elective course

Course Number: ACC/TAX 920

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.

"AEOI: Is it really the end of tax evasion based on residence?"

The seminar is held by Elisa Casi together with Mark Orlic and Sara Nenadic from PwC Frankfurt. For paper please contact Ms. Gabi Riedlinger under accounting(at)bwl.uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.10.17
Wednesday
13:45
15:15
SO 133
25.10.17
Wednesday
13:45
15:15
SO 133
15.11.17
Wednesday
13:45
14:30
SO 133
Seminar
AEOI: Is it really the end of tax evasion based on residence?
13.12.17
Wednesday
10:00
10:45
SO 318

Lecturer(s)


Course Type: core course

Course Number: E 703

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.17
Friday
08:30
18:45
L9, 7 – room 308
10.10.17
12.12.17
Tuesday
10:15
11:45
L9, 1-2 – room 002
12.10.17
14.12.17
Thursday
10:15
11:45
L9, 1-2 – room 409
Tutorial
11.10.17
13.12.17
Wednesday
12:00
13:30
L7, 3-5, room 257
13.10.17
15.12.17
Friday
13:45
15:15
SO 133


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.

 

For more detailed information about the first sessions of this course, click here.

 

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.

 

Teaching Assistants:

Daria Khromenkova (CDSE)

Exercise Groups 1 + 2

 

Sebastian Merkel (CDSE)

Exercise Groups 3 + 4


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.17
Monday
10:15
11:45
Room C012, Building A5, 6
05.09.17
Tuesday
10:15
11:45
Room C012, Building A5, 6
06.09.17
Wednesday
10:15
11:45
Room C012, Building A5, 6
07.09.17
Thursday
10:15
11:45
Room C012, Building A5, 6
Final Exam Date
05.10.17
Thursday
17:15
19:15
Room C013, Building A5, 6
Retake
14.12.17
Thursday
17:15
19:15
C012 in A5,6 Bauteil C
Tutorial
Exercise Group 1
04.09.17
25.09.17
Monday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
04.09.17
25.09.17
Monday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
04.09.17
25.09.17
Monday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
04.09.17
25.09.17
Monday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
05.09.17
26.09.17
Tuesday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
05.09.17
26.09.17
Tuesday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
05.09.17
26.09.17
Tuesday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
05.09.17
26.09.17
Tuesday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
06.09.17
27.09.17
Wednesday
13:45
15:15
Room A301, Building B6, 23-25
Exercise Group 3
06.09.17
27.09.17
Wednesday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
06.09.17
27.09.17
Wednesday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
06.09.17
27.09.17
Wednesday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
07.09.17
28.09.17
Thursday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
07.09.17
28.09.17
Thursday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
07.09.17
28.09.17
Thursday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
07.09.17
28.09.17
Thursday
15:30
17:00
Room A303, Building B6, 23-25

Lecturer(s)


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.

 

Teaching Assistant:

Larionov


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
09.10.17
06.12.17
Monday
10:15
11:45
Room 001, Building L7, 3-5 (tbc)
09.10.17
06.12.17
Wednesday
10:15
11:45
Room 001, Building L7, 3-5 (tbc)
Single Date
03.11.17
Friday
12:00
13:30
SO 318, Schneckenhof Ost
Written Exam
20.12.17
WED
08:30
10:30
L7, 3-5, 001
Retake
29.01.18
Monday
10:15
12:15
room 212/213, B6, 30-32
Tutorial
Exercise Group 1
09.10.17
06.12.17
Monday
12:00
13:30
Room 001, Building L7, 3-5
Exercise Group 2
09.10.17
06.12.17
Monday
13:45
15:15
Room 001, Building L7, 3-5

Lecturer(s)


Course Type: core course

Course Number: FIN 801

Credits: 8

Prerequisites

E 700


Course Content

The aim of this course is to provide students with the foundations of financial economics in a rigorous way. The course contains a lecture component with advanced exercise sessions and a seminar component where students are asked to prepare a term paper on one of the topics covered in class. We start by discussing several key concepts from the theory of choice (also known as utility theory), including the axioms of expected utility, risk aversion and stochastic dominance. We then move to the portfolio theory and capital market equilibrium (CAPM and APT). The focus of the course is on the consumption-based approach to asset pricing. We introduce concepts such as the stochastic discount factor (or pricing kernel), contingent claims and risk-neutral valuation, and consider the beta representation framework and examples of factor pricing models. We study single- and multi-period models and look eventually at the role of information for asset pricing.

Form of Assessment: Written Exam (90 minutes) 60%, Class Participation (incl. term paper) 40%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.17
30.10.17
Monday
17:00
18:30
SO 133
15.09.17
17.11.17
Friday
10:15
11:45
SO 133
24.11.17
Friday
08:00
14:00
L9, 1-2, room 409

Lecturer(s)


Course Type: elective course

Course Number: FIN 911

Credits: 8

Course Content

This is a restricted course for students who are currently doctoral students at the CDSB of the University of Mannheim. It is intended for beginning as well as advanced doctoral students up to the stage where they might already plan their academic career

The Finance Area of the University of Mannheim organizes a faculty seminar with an impressive list of international speakers. This course offers the opportunity to benefit even more from this seminar series by giving students the possibility to discuss the paper beforehand and meet the speakers in an informal atmosphere. The ultimate goal of this course is to get in touch with the newest research from different fields of finance and to ideally generate new research ideas based on the discussion of the speaker’s papers and the direct interaction with our guests.

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 Area Seminar. The class will take place on selected Mondays during the semester when seminar presentation by an external speaker are given.


For updates on the schedule, please regularly consult the following webpage: http://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 10-15 min). 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 the paper that he or she is discussing. 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”). Frequency of the meetings will depend on the number of course participants. We plan to have about 6 to 10 course meetings.

Learning Outcomes: 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. 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 often 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.

Form of assessment: Presentation of Paper 30%, Discussion of Paper 25%, Referee Report 25%, Oral Participation 20% 

Students are required to participate in the morning classes and the “Cookies with Speaker” sessions. Regular participation in both session 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 more than two meetings, 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.

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


Please note: Professor Dr. Ruenzi reserves 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.


Kick-off Meeting: Monday, September 4, 2017


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.17
04.12.17
Monday
12:00
13:30
L9, 7 – room 308

Lecturer(s)


Course Type: core course

Course Number: IE 710

Credits: 5

Course Content

The course presents methods for the computer assisted automatic analysis of digital documents as a basis for further quantitative content analyses used in social and cultural sciences.

In the beginning we will present some possible analyses computational linguistics can offer to social and cultural sciences using various tools. This is followed by a short programming course in the Python programming language introducing a more flexible way of preprocessing texts and also access to text data through web crawling and conversion of different file formats. More advanced methods on text classification and clustering are presented later on along with more tools that can be used. In the final part of the course participants will present their own project work to each other.

Form of assessment:  Implementation of a project, final presentation, report (~ 15 pages)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
20.09.17
06.12.17
Wednesday
15:30
18:45
A5, 6 – room C-109

Lecturer(s)


Course Type: core course

Course Number: IS 801

Credits: 8

Course Content

Since the 90’s information and communication technology (ICT) has fundamentally changed the way organizations are conducting business. Organizations and the entire society are challenged with the effective design, delivery, use, and impact of ICT. The IS discipline addresses this challenge and investigates the phenomena that emerge when the technological and the social system interact. A decade ago, an intensive discussion on the relevancy and impact of IS research has started. In this context, several scholars have suggested that the IS community returns to an exploration of the "IT" that underlies the discipline. Design research has potentials to address this challenge. As such, it is nothing new: Design can be found in many disciplines and fields, notably Engineering and Computer Science, using a variety of approaches, methods, and techniques.

This course intends to provide a comprehensive overview on design science in IS research from different perspectives: basic definitions, principles and theoretical foundations, frameworks and methodologies, theory building, as well as design science research examples published in top journals.

Learning outcomes: PhD students are introduced to the exciting field of design science research. They understand the basic principles for successfully carrying out design science research.

Form of assessment: Assignment, Presentation, Discussion


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.09.17
05.12.17
Tuesday
13:45
15:15
L 15, 1-6, room 422/423


Course Type: core course

Course Number: IS 901

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 student 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 the literature in order to apply and reinforce the epistemological stance presented. Assignment of topics will be conducted by the lecturer.

The course is particularly helpful in preparing students for understanding the basic philosophical assumptions they either implicitly or explicitly make when they do research. The course is largely theoretical; designed to provide a perspective on the current research literature so that students learn how to identify different research orientations and build an informed opinion on critical research issues which will help them in developing their dissertations. The primary focus of the course is on the current research programs in IS and the philosophical assumptions which underlie them. We’ll also look at underlying philosophical assumptions adopted in the Ops Mgt / Decision Sciences area. The course will explore the various schools of thought which exist, analyzing their special sets of assumptions which distinguish one from another. The most important assumptions relate to the nature of the world around us (ontology), and how to acquire knowledge about it (epistemology). Different research epistemologies can be characterized by the ideal of knowledge to which each of them adheres, and the particular preferred approaches for obtaining knowledge. As not all epistemologies are equally well represented in actual research programs, orthodox and "emerging" research programs will be explored. The course will also consider how other Management Studies disciplines deal with the same fundamental issues faced by IS researchers (e.g. what is truth, what is knowledge).


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.10.17
Wednesday
08:30
13:30
L15, 1-6 – room 714/15
06.10.17
Friday
08:30
13:30
L15, 1-6 – room 714/15
09.10.17
Monday
08:30
13:30
L15, 1-6 – room 714/15
11.10.17
Wednesday
08:30
11:30
L15, 1-6 – room 714/15

Lecturer(s)


Course Type: core course

Course Number: MAN 802

Credits: 6

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).

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".

Learning outcomes: This course aims to provide a basic understanding of the theory and management of nonprofit organizations. Each student will be asked to read 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.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
12.09.17
Tuesday
14:30
16:30
L5, 4 – room 207/209
Q & A-Session
17.10.17
Tuesday
14:30
16:30
L5, 4 – room 207/209
Presentation Session
13.11.17
Monday
10:00
17:00
L5, 4 – room 207/209

Lecturer(s)


Course Type: core course

Course Number: MAN 805

Credits: 6

Course Content

This module offers an overview of the statistical procedures and methods that are relevant in management research. After having gained a broad understanding of the methods that are important in the respective literatures, students integrate this knowledge by examining some exemplary research studies for each method and by asking how they would go about in conducting their own research in this field. Students apply their knowledge from the seminar presentations in several exercises.

In particular, the course covers the following topics:

  • Moderation and Mediation
  • Control Variables
  • Scales and scale analysis
  • Common Method Variance
  • Hypothesis testing
  • Outliers
  • Multicollinearity
  • Missing data
  • Multilevel modelling

Learning outcomes: By the end of the module students will be able to:

  • Identify issues and problems in quantitative management research
  • Perform statistical analyses in selected areas (e.g., multilevel modeling and scale analysis)
  • Design quantitative research projects that consider contemporary standards and suggestions in management research
  • Learn how to address methodological issues in research papers

Form of assessment: Oral exam, Presentation


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.10.17
Friday
09:00
17:00
O 326
20.10.17
Friday
09:00
17:00
O 326
10.11.17
Friday
09:00
17:00
O 326

Lecturer(s)


Course Type: core course

Course Number: MAN 806

Credits: 6

Course Content

Students will gain an overview of fundamental topics in the fields of organization and innovation. The course starts with a kick-off. A list of required readings and a detailed course program will be provided at this meeting. Then, students have one month to prepare their input for the blocked seminar. During the blocked seminar, the papers, they will have read and prepared, will be presented and discussed. Afterwards there will be a general discussion. Besides the content itself, conceptual framing and methodology (strengths and weaknesses) will be reviewed. The papers selected for presentation will cover different quantitative and qualitative methods.

Students will learn to critically assess existing literature, to formulate research questions, to frame theoretical contributions and to design and implement a research design to be able to derive causal results.

Form of Assessment: Presentation 50%, Discussion 50%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
19.09.17
Tuesday
13:45
17:00
O 048
23.10.17
Monday
14:00
18:30
L9, 1-2, room 001
24.10.17
Tuesday
09:00
18:30
L9, 1-2, room 001

Lecturer(s)


Course Type: core course

Course Number: E 703

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.17
Friday
08:30
18:45
L9, 7 – room 308
10.10.17
12.12.17
Tuesday
10:15
11:45
L9, 1-2 – room 002
12.10.17
14.12.17
Thursday
10:15
11:45
L9, 1-2 – room 409
Tutorial
11.10.17
13.12.17
Wednesday
12:00
13:30
L7, 3-5, room 257
13.10.17
15.12.17
Friday
13:45
15:15
SO 133

Lecturer(s)


Course Type: core course

Course Number: MKT 801

Credits: 6

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.

By the end of the course, students should be able to use fundamental research concepts gained in the course in designing and evaluating research in marketing.


Form of assessment: Paper 30%, Presentation 70%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.09.17
08.12.17
Friday
10:15
11:45
L9, 1-2, room 009

Lecturer(s)


Course Type: core course

Course Number: MKT 903

Credits: 6

Course Content

The goal of the course is to provide Ph.D. students an introduction in and overview of state-of-the-art discrete choice methods in business and marketing research. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models 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. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. The course will also cover procedures for endogeneity and expectation-maximization algorithms. Participants will study a variety of articles and case studies which demonstrate the application of such models to real business 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

 

Form of assessment: Written Exam (60 minutes) 50%, Home Assignments 50%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.17
Friday
09:30
18:00
L5, 1, room 009
13.10.17
Friday
09:30
18:00
L5, 1, room 009
26.10.17
Thursday
09:30
17:00
17.11.17
Friday
09:00
18:00

Lecturer(s)


Course Type: core course

Course Number: OPM 805

Credits: 8

Course Content

The goal of this seminar is to introduce the participants to the conducting of scientific research. It thereby prepares them for the writing of their dissertation proposal. Participants will carry out a literature study on a given topic in the domain of business analytics and discuss the results in a written report and in an oral presentation.

Learning outcomes: Students will learn how to analyze the academic literature on a given topic. They will become acquainted with the setup and composition of academic publications. They will also learn how to the present the results of their analysis.

Form of assessment: Paper 70 %, Presentation 30 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-Off
11.09.17
Monday
08:30
09:45
SO 322


Course Type: core course

Course Number: OPM 901

Credits: 8

Course Content

This course aims at PhD students in business administration. 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 done together in class.

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

Form of assessment: Presentation, Assignment


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.09.17
07.12.17
Thursday
12:00
13:30
SO 318

Lecturer(s)


Course Type: elective course

Course Number: OPM 801

Credits: 8

Prerequisites

Recommended: Fundamentals in mathematics (including Linear Programming), knowledge of GAMS modelling language


Course Content

This course aims at Ph.D. students in information systems, business administration, and computer science. It provides a basic understanding of linear and mixed-integer optimization models and solution methods. The course is partly taught in a seminar-style format. Allocation of topics will be done together in the class.

Learning outcomes: 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 solve them with standard software, and how to construct heuristic solution algorithms. The students learn to deal with the complexity of real-world problems via aggregation, relaxation, and decomposition techniques.

Form of assessment: Assignment, Presentation, Class Participation


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
19.10.17
Thursday
08:30
20:00
SO 322
20.10.17
Friday
08:30
20:00
SO 318
10.11.17
Saturday
08:30
20:00
SO 322

Lecturer(s)


Course Type: elective course

Course Number: OPM 803

Credits: 8

Prerequisites

Recommended: 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.

Learning outcomes: 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.

Form of assessment: Assignment, Presentation, Class Participation


 

Please note: On October 20, the course starts at 12:00.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.10.17
03.11.17
Friday
12:00
17:00
SO 322
17.11.17
Friday
12:00
15:15
SO 322

Lecturer(s)


Course Type: core course

Course Number: E 703

Credits: 8

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

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.17
Friday
08:30
18:45
L9, 7 – room 308
10.10.17
12.12.17
Tuesday
10:15
11:45
L9, 1-2 – room 002
12.10.17
14.12.17
Thursday
10:15
11:45
L9, 1-2 – room 409
Tutorial
11.10.17
13.12.17
Wednesday
12:00
13:30
L7, 3-5, room 257
13.10.17
15.12.17
Friday
13:45
15:15
SO 133


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.

 

For more detailed information about the first sessions of this course, click here.

 

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.

 

Teaching Assistants:

Daria Khromenkova (CDSE)

Exercise Groups 1 + 2

 

Sebastian Merkel (CDSE)

Exercise Groups 3 + 4


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.09.17
Monday
10:15
11:45
Room C012, Building A5, 6
05.09.17
Tuesday
10:15
11:45
Room C012, Building A5, 6
06.09.17
Wednesday
10:15
11:45
Room C012, Building A5, 6
07.09.17
Thursday
10:15
11:45
Room C012, Building A5, 6
Final Exam Date
05.10.17
Thursday
17:15
19:15
Room C013, Building A5, 6
Retake
14.12.17
Thursday
17:15
19:15
C012 in A5,6 Bauteil C
Tutorial
Exercise Group 1
04.09.17
25.09.17
Monday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
04.09.17
25.09.17
Monday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
04.09.17
25.09.17
Monday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
04.09.17
25.09.17
Monday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
05.09.17
26.09.17
Tuesday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
05.09.17
26.09.17
Tuesday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
05.09.17
26.09.17
Tuesday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
05.09.17
26.09.17
Tuesday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
06.09.17
27.09.17
Wednesday
13:45
15:15
Room A301, Building B6, 23-25
Exercise Group 3
06.09.17
27.09.17
Wednesday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
06.09.17
27.09.17
Wednesday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
06.09.17
27.09.17
Wednesday
15:30
17:00
Room A303, Building B6, 23-25
Exercise Group 1
07.09.17
28.09.17
Thursday
13:45
15:15
Room A302, Building B6, 23-25
Exercise Group 3
07.09.17
28.09.17
Thursday
13:45
15:15
Room A303, Building B6, 23-25
Exercise Group 2
07.09.17
28.09.17
Thursday
15:30
17:00
Room A302, Building B6, 23-25
Exercise Group 4
07.09.17
28.09.17
Thursday
15:30
17:00
Room A303, Building B6, 23-25

Lecturer(s)


Course Type: elective course

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
  • http://theanalysisofdata.com/probability/0_1.html

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
08.09.17
22.09.17
Friday
08:30
18:45
O 048
13.10.17
Friday
15:30
18:45
L9, 7 – room 308
20.10.17
Friday
08:30
18:45
L9, 7 - room 308


Course Type: elective course

Course Number: ACC/TAX 920

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.

"AEOI: Is it really the end of tax evasion based on residence?"

The seminar is held by Elisa Casi together with Mark Orlic and Sara Nenadic from PwC Frankfurt. For paper please contact Ms. Gabi Riedlinger under accounting(at)bwl.uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
04.10.17
Wednesday
13:45
15:15
SO 133
25.10.17
Wednesday
13:45
15:15
SO 133
15.11.17
Wednesday
13:45
14:30
SO 133
Seminar
AEOI: Is it really the end of tax evasion based on residence?
13.12.17
Wednesday
10:00
10:45
SO 318

Lecturer(s)


Course Type: elective course

Course Number: TAX 916

Credits: 8

Course Content

The course gives an applied introduction to the methodology employed in the empirical research literature. The main topics include: Ordinary least squares, instrumental variables estimation, and panel data econometrics. Further topics may also be included according to demand by participants.

The covered material 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.

Form of assessment: Oral exam (10 minutes) 50%, Class Participation 50%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
14.09.17
28.09.17
Thursday
12:00
13:30
O 254
18.09.17
25.09.17
Monday
08:30
10:00
O 254
02.10.17
18.12.17
Monday
10:15
11:45
O 226/28
04.10.17
20.12.17
Wednesday
10:15
11:45
O 048

Register

Business Fall 2017

ACC 902
Normative Accounting Research
ACC 911
Brown Bag - Research Development Workshop
E 703
Advanced Econometrics I
E700
Mathematics for Economists
E701
Advanced Microeconomics I
Statistics Refresher
ACC 905
Applied Methods & Tools in Empirical Accounting Research (Paper Replication)
ACC 906
Model Development Workshop
ACC/TAX 920
Brown Bag Seminar Empirical Accounting & Taxation
FIN 801
Discrete-Time Finance
FIN 911
Current Research Topics in Finance
IE 710
Computer-based Content Analysis
IS 801
Design Science Research
IS 901
Epistemological Foundations
MAN 802
Fundamentals of Non-Profit Management Science
MAN 805
Applied Methods in Management Research
MAN 806
Advances in Organization and Innovation Research
MKT 801
Fundamentals of Marketing Research
MKT 903
Advanced Business Econometrics
OPM 805
Research Seminar Business Analytics
OPM 901
Research Seminar Operations Management & Operations Research
OPM 801
Optimization and Heuristics
OPM 803
Selected Topics in Nonlinear Optimization
TAX 916
Applied Econometrics