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

Fall 2020

Course Catalog

Fall 2020

Lecturer(s)


Course Type: core course

Course Number: ACC 905

Credits: 8

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 %


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.20
11.12.20
Friday
13:45
17:00
O 326/28 or BWL-ZOOM-06


Course Type: core course

Course Number: ACC/TAX 910

Course Content

The course focuses on current research topics in the field of accounting and taxation. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. For each presentation, a separate preparation session for the Ph.D. students is offered in advance by rotating faculty. Overall, the course deepens the students’ insights into a variety of research methods that are currently popular in empirical and theoretical research.

Learning outcomes: Students will learn to follow-up with and discuss about current research topics in accounting and taxation. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Seminar Dates are announced here.



Course Type: core course

Course Number: ACC/TAX 920

Course Content

The course is taught in a seminar-style format. Students present their own research ideas at different stages of the project (early ideas, preliminary results, and complete working papers). The presentations involve an interactive discussion between faculty and students about the project’s potential contribution, related literature, research design and interpretation of results.

Learning outcomes: Students will learn how to present and discuss their own research results in a scientific format. They will become acquainted with acting as a discussant for other topics. Students will gain insights into the assessment of contribution, research design, and interpretation of research papers. The development of these skills is also helpful for writing scientific referee reports.


Coursedates will be announced via email to registered participants.


Lecturer(s)


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.

 

Teaching Assistants

Exercise Session 1
Tutor: Thi Anh Dam
Time: from 13.45 to 15:15
Supporter: Giovanni Ballarin

Exercise Session 2
Tutor: Giovanni Ballarin
Time: from 15:30 to 17:00
Supporter: Dam Thi Anh


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
07.09.20
01.10.20
Monday - Thursday
10:15
11:45
online
Exam
08.10.20
Thursday
08:00
10:00
001. A (A 3 Bibl.,Hörsaalgebäude)
Retake Exam
03.12.20
Thursday
14:00
16:00
O 148
Tutorial
Session 1 - Tutor: Dam
07.09.20
01.10.20
Monday - Thursday
13:45
15:15
online
Session 2 - Tutor: Ballarin
07.09.20
01.10.20
Monday - Thursday
15:30
17:00
online


Course Type: core course

Course Number: E701

Credits: 8

Prerequisites

Mathematics for Economists, intermediate knowledge of microeconomics


Course Content

The course is aimed at doctoral students in all fields of business administration. It provides an introduction to microeconomic theory on a graduate level, highlighting aspects which are of specific relevance for business research.

The main topics covered include:

  1. Theory of consumer choice under certainty and uncertainty
  2. Theory of the firm, production cost and supply
  3. Markets, equilibrium, welfare
  4. Strategic behavior under complete and incomplete information
  5. Incentives and asymmetric information

The objective of the course is to enable doctoral students in business to read and discuss economic theory and to equip them with the tools to formulate their own research based on theoretically grounded hypotheses.

Learning outcomes: Understanding and critically evaluating the fundamental concepts of microeconomic theory, game theory and mechanism design; learning the relevant tools and underlying assumptions for economic analysis in ongoing research.

Form of assessment: Midterm and final exams: 80-100%, Exercises: Up to 20%


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.20
08.12.20
Tuesday
15:30
17:00
O 151 + ZOOM
15.10.20
10.12.20
Thursday
15:30
17:00
O 151 + ZOOM
Tutorial
Q&A
15.10.20
10.12.20
Thursday
13:45
15:15
O 151 + ZOOM
19.10.20
07.12.20
Monday
13:45
15:00
O 151 + ZOOM

Lecturer(s)


Course Type: core course

Course Number: E703

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.


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.20
08.12.20
Tuesday
10:15
11:45
O 151 or BWL-ZOOM 15
15.10.20
10.12.20
Thursday
10:15
11:45
O 151 or BWL-ZOOM 14
Tutorial
07.10.20
09.12.20
Wednesday
15:30
17:00
L 7, 3-5, room 358 or BWL-ZOOM 20
09.10.20
11.12.20
Friday
13:45
15:15
O 151 or BWL-ZOOM 10

Lecturer(s)


Course Type: core 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%


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.10.20
07.12.20
Monday
08:30
10:00
O 048 or BWL-ZOOM-14
07.10.20
09.12.20
Wednesday
08:30
10:00
O 048 or BWL-ZOOM-14

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
  • 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.


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.20
09.10.20
Friday
08:30
18:45
BWL ZOOM-03

Lecturer(s)


Course Type: elective course

Credits: 6

Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


The course is also part of the TRR 266 Accounting for Transparency



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.20
Tuesday
10:00
17:00
ZOOM
17.09.20
Thursday
10:00
17:00
ZOOM
21.09.20
Monday
10:00
17:00
ZOOM

Lecturer(s)


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.

 

Teaching Assistants

Exercise Session 1
Tutor: Thi Anh Dam
Time: from 13.45 to 15:15
Supporter: Giovanni Ballarin

Exercise Session 2
Tutor: Giovanni Ballarin
Time: from 15:30 to 17:00
Supporter: Dam Thi Anh


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
07.09.20
01.10.20
Monday - Thursday
10:15
11:45
online
Exam
08.10.20
Thursday
08:00
10:00
001. A (A 3 Bibl.,Hörsaalgebäude)
Retake Exam
03.12.20
Thursday
14:00
16:00
O 148
Tutorial
Session 1 - Tutor: Dam
07.09.20
01.10.20
Monday - Thursday
13:45
15:15
online
Session 2 - Tutor: Ballarin
07.09.20
01.10.20
Monday - Thursday
15:30
17:00
online


Course Type: core course

Course Number: E701

Credits: 8

Prerequisites

Mathematics for Economists, intermediate knowledge of microeconomics


Course Content

The course is aimed at doctoral students in all fields of business administration. It provides an introduction to microeconomic theory on a graduate level, highlighting aspects which are of specific relevance for business research.

The main topics covered include:

  1. Theory of consumer choice under certainty and uncertainty
  2. Theory of the firm, production cost and supply
  3. Markets, equilibrium, welfare
  4. Strategic behavior under complete and incomplete information
  5. Incentives and asymmetric information

The objective of the course is to enable doctoral students in business to read and discuss economic theory and to equip them with the tools to formulate their own research based on theoretically grounded hypotheses.

Learning outcomes: Understanding and critically evaluating the fundamental concepts of microeconomic theory, game theory and mechanism design; learning the relevant tools and underlying assumptions for economic analysis in ongoing research.

Form of assessment: Midterm and final exams: 80-100%, Exercises: Up to 20%


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.20
08.12.20
Tuesday
15:30
17:00
O 151 + ZOOM
15.10.20
10.12.20
Thursday
15:30
17:00
O 151 + ZOOM
Tutorial
Q&A
15.10.20
10.12.20
Thursday
13:45
15:15
O 151 + ZOOM
19.10.20
07.12.20
Monday
13:45
15:00
O 151 + ZOOM

Lecturer(s)


Course Type: core course

Course Number: E703

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.


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.20
08.12.20
Tuesday
10:15
11:45
O 151 or BWL-ZOOM 15
15.10.20
10.12.20
Thursday
10:15
11:45
O 151 or BWL-ZOOM 14
Tutorial
07.10.20
09.12.20
Wednesday
15:30
17:00
L 7, 3-5, room 358 or BWL-ZOOM 20
09.10.20
11.12.20
Friday
13:45
15:15
O 151 or BWL-ZOOM 10

Lecturer(s)


Course Type: core course

Course Number: FIN 801

Credits: 8

Prerequisites

Formal: E 700 (parallel attendance possible)

Recommended: We assume background knowledge of mathematics (matrix algebra) and econometrics.


Course Content

This course introduces the theoretical foundations of modern discrete-time asset pricing theory and the empirical methods used to test asset pricing models. The course contains a lecture component with exercise sessions and a colloquium where students present a term paper on a topic related to the contents of the course.

The course will cover key concepts from the theory of choice (also known as utility theory) and then move on to the theory of portfolio selection and models of capital market equilibrium (CAPM and APT). Particular emphasis will be put 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. The theory part concludes with a discussion of the role of information for asset prices.

In the empirical part students will be familiarized with the classical and modern approaches to test asset pricing models empirically. Based on these foundations we will then discuss the most recent empirical research on asset pricing.

Learning outcomes:  The aim of this course is to (1) provide students with the theoretical foundations of asset pricing theory and (2) introduce students into the empirical methodology used to empirically test asset pricing models. Particular emphasis will be put on the most recent academic research.

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


 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.10.20
07.12.20
Monday
10:15
11:45
BWL-ZOOM-19
14.10.20
25.11.20
Wednesday
10:15
11:45
BWL-ZOOM-19
21.10.20
Wednesday
12:00
13:30
BWL-ZOOM-19
25.11.20
Wednesday
12:00
13:30
BWL-ZOOM-19
14.12.20
Monday
09:00
17:00
BWL-ZOOM-2

Lecturer(s)


Course Type: core course

Course Number: FIN 910

Course Content

The course focuses on current research topics in the field of finance. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. The course introduces students to the variety of research methods that are currently popular in empirical and theoretical research.

Learning outcomes: Students will learn to follow-up with and discuss about current research topics in finance. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Form of assessment: Oral participation.


Seminar Dates are announced here.


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
  • 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.


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.20
09.10.20
Friday
08:30
18:45
BWL ZOOM-03

Lecturer(s)


Course Type: elective course

Credits: 6

Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


The course is also part of the TRR 266 Accounting for Transparency



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.20
Tuesday
10:00
17:00
ZOOM
17.09.20
Thursday
10:00
17:00
ZOOM
21.09.20
Monday
10:00
17:00
ZOOM

Lecturer(s)


Course Type: elective course

Course Number: FIN 922

Credits: 4

Prerequisites

Formal: Students must have passed their first-year courses.

Recommended: Willingness to read, discuss, challenge, engage and think for yourself is critical for this course.


Students need to have both books available at the start of the course.


Course Content

Financial and other markets play a key role for the world we live in. This course is an attempt to help us come to grips with central questions for economics and the world: what is the proper role for markets in society? When do markets work well? When and how should we regulate them? Is there a role for morals in markets; and if so, what is it? We will do this by reading and discussing two eminent books on markets: first, perhaps one of the most influential books on markets every written, Milton Friedman’s Capitalism and Freedom, University of Chicago Press. Second, Samuel Bowles’ TheMoral Economy, Yale University Press, which represents a more recent approach to understanding markets. We will complement the perspectives laid out in the books by additional material provided by the instructor. Students need to be willing to read both books, form their own opinions on them, and elaborate on and defend their views in a final write-up and group discussions.

Learning outcomes: The aim of this course is to engage in intellectual dialogue, to develop a personal point of view on some of the central economic questions we face today, and to allow ourselves to think creatively about the future. After completing this course, students will have read two important texts on the role of markets for society, they will have trained their ability to distill an own point of view from the writings of leading economists, they will train their presentation, writing and discussion skills, and they will train to creatively apply what they have read in writing about the future of markets in our society.

Form of assessment: Assignment 30 %, Presentation 20 %, Class Participation 50 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
28.09.20
07.12.20
Monday / every second week
10:15
13:15
L9, 1-2, room 003

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
07.09.20
07.12.20
Monday
15:30
17:00
BWL-ZOOM-10

Lecturer(s)


Course Type: core course

Course Number: IS 901

Credits: 8

Course Content

This course is designed for doctoral students in information systems and other managerial disciplines. 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. Assignment of topics will be conducted by the lecturer.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
18.09.20
Friday
13:45
17:00
L 15, 1-6 - room A 001
02.10.20
Friday
13:45
17:00
L 15, 1-6 - room A 001
16.10.20
Friday
13:45
17:00
L 15, 1-6 - room A 001
30.10.20
Friday
13:45
17:00
L 15, 1-6 - room A 001
13.11.20
Friday
13:45
17:00
L 15, 1-6 - room A 001
27.11.20
Friday
13:45
17:00
L 15, 1-6 - room A 001
20.01.21
Friday
13:45
17:00
L 15, 1-6 - room A 001


Course Type: core course

Course Number: IS/OPM 910

Course Content

The course focuses on current research topics in the field of information systems and operations management. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The course introduces students to the variety of research methods that are currently popular in empirical and theoretical research.

Learning outcomes: Students will learn to follow-up with and discuss about current research topics in information systems and operations management. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Form of assessment: Oral participation.

Seminar Dates are announced here.


Lecturer(s)


Course Type: elective course

Credits: 6

Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


The course is also part of the TRR 266 Accounting for Transparency



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.20
Tuesday
10:00
17:00
ZOOM
17.09.20
Thursday
10:00
17:00
ZOOM
21.09.20
Monday
10:00
17:00
ZOOM

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
28.09.20
Monday
14:30
16:30
Q&A-Session
19.10.20
Monday
14:30
16:00
23.11.20
Monday
09:00
17:00

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
28.09.20
Monday
14:30
16:30
Q&A-Session
19.10.20
Monday
14:30
16:00
23.11.20
Monday
09:00
17:00

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 (20 minutes) 75 %, presentation 25 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.10.20
Monday
09:00
13:00
12.10.20
Monday
09:00
13:00
26.10.20
Monday
09:00
13:00
02.11.20
Monday
09:00
13:00
09.11.20
Monday
09:00
13:00

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 (20 minutes) 75 %, presentation 25 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
05.10.20
Monday
09:00
13:00
12.10.20
Monday
09:00
13:00
26.10.20
Monday
09:00
13:00
02.11.20
Monday
09:00
13:00
09.11.20
Monday
09:00
13:00

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
22.09.20
Tuesday
14:00
17:00
BWL-ZOOM-01
27.10.20
Tuesday
09:00
18:00
BWL-ZOOM-01
03.11.20
Tuesday
09:00
18:00
BWL-ZOOM-01

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
22.09.20
Tuesday
14:00
17:00
BWL-ZOOM-01
27.10.20
Tuesday
09:00
18:00
BWL-ZOOM-01
03.11.20
Tuesday
09:00
18:00
BWL-ZOOM-01


Course Type: core course

Course Number: MAN 910

Course Content

The course focuses on current research topics in the field of management. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. The course introduces students to the variety of research methods that are currently popular in empirical and theoretical research.

Learning outcomes: Students will learn to follow-up with and discuss about current research topics in accounting and taxation. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Seminar Dates are announced here.



Course Type: core course

Course Number: MAN 910

Course Content

The course focuses on current research topics in the field of management. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. The course introduces students to the variety of research methods that are currently popular in empirical and theoretical research.

Learning outcomes: Students will learn to follow-up with and discuss about current research topics in accounting and taxation. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Seminar Dates are announced here.


Lecturer(s)


Course Type: elective course

Credits: 6

Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


The course is also part of the TRR 266 Accounting for Transparency



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.20
Tuesday
10:00
17:00
ZOOM
17.09.20
Thursday
10:00
17:00
ZOOM
21.09.20
Monday
10:00
17:00
ZOOM


Course Type: elective course

Course Number: MAN 809

Credits: 6

Course Content

Mathematical models and formal logic have been gaining ground as tools for theory construction in the social sciences and have arguably become dominant in economics. The vast majority of papers in management and the disciplines of psychology and sociology nevertheless continue to build their arguments verbally. This course exposes students to techniques for how to analyze these verbal theories and how to construct coherent theoretical arguments without the use of a formal language. The course will draw on examples from (technological) innovation management, organization theory, and sociology, but it will not attempt to survey comprehensively any particular substantive topic in those literatures. Students should therefore view the course as a complement to, rather than as a substitute for, subject- based courses. By extension, the course invites students from all disciplines who are interested in complementing their education with a basic exposure to theory construction.

Learning outcomes: In essence, the course provides an opportunity to compose the front section of an academic manuscript and receive constructive feedback.

Form of assessment: Assignment 40 %, Paper 50 %, Class Participation 10 %



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.10.20
Wednesday
10:00
13:00
BWL-ZOOM-04
10.11.20
Tuesday
10:00
17:00
BWL-ZOOM-03


Course Type: elective course

Course Number: MAN 809

Credits: 6

Course Content

Mathematical models and formal logic have been gaining ground as tools for theory construction in the social sciences and have arguably become dominant in economics. The vast majority of papers in management and the disciplines of psychology and sociology nevertheless continue to build their arguments verbally. This course exposes students to techniques for how to analyze these verbal theories and how to construct coherent theoretical arguments without the use of a formal language. The course will draw on examples from (technological) innovation management, organization theory, and sociology, but it will not attempt to survey comprehensively any particular substantive topic in those literatures. Students should therefore view the course as a complement to, rather than as a substitute for, subject- based courses. By extension, the course invites students from all disciplines who are interested in complementing their education with a basic exposure to theory construction.

Learning outcomes: In essence, the course provides an opportunity to compose the front section of an academic manuscript and receive constructive feedback.

Form of assessment: Assignment 40 %, Paper 50 %, Class Participation 10 %



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.10.20
Wednesday
10:00
13:00
BWL-ZOOM-04
10.11.20
Tuesday
10:00
17:00
BWL-ZOOM-03

Lecturer(s)


Course Type: core course

Course Number: E703

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.


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.20
08.12.20
Tuesday
10:15
11:45
O 151 or BWL-ZOOM 15
15.10.20
10.12.20
Thursday
10:15
11:45
O 151 or BWL-ZOOM 14
Tutorial
07.10.20
09.12.20
Wednesday
15:30
17:00
L 7, 3-5, room 358 or BWL-ZOOM 20
09.10.20
11.12.20
Friday
13:45
15:15
O 151 or BWL-ZOOM 10

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.

Learning outcomes: 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: Essay: 30%, Presentation: 70%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.10.20
03.12.20
Thursday
13:45
15:15
BWL-ZOOM-11

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
23.10.20
27.11.20
Friday
09:30
17:00
L 5, 2 - room 107

Lecturer(s)


Course Type: core course

Course Number: MKT 910

Course Content

The course focuses on current research topics in the field of marketing. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. The course introduces students to the variety of research methods that are currently popular in empirical and theoretical research.

Learning outcomes: Students will learn to follow-up with and discuss about current research topics in accounting and taxation. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Seminar Dates are announced here.


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
  • 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.


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.20
09.10.20
Friday
08:30
18:45
BWL ZOOM-03

Lecturer(s)


Course Type: elective course

Credits: 6

Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


The course is also part of the TRR 266 Accounting for Transparency



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.20
Tuesday
10:00
17:00
ZOOM
17.09.20
Thursday
10:00
17:00
ZOOM
21.09.20
Monday
10:00
17:00
ZOOM

Lecturer(s)


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.

 

Teaching Assistants

Exercise Session 1
Tutor: Thi Anh Dam
Time: from 13.45 to 15:15
Supporter: Giovanni Ballarin

Exercise Session 2
Tutor: Giovanni Ballarin
Time: from 15:30 to 17:00
Supporter: Dam Thi Anh


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
07.09.20
01.10.20
Monday - Thursday
10:15
11:45
online
Exam
08.10.20
Thursday
08:00
10:00
001. A (A 3 Bibl.,Hörsaalgebäude)
Retake Exam
03.12.20
Thursday
14:00
16:00
O 148
Tutorial
Session 1 - Tutor: Dam
07.09.20
01.10.20
Monday - Thursday
13:45
15:15
online
Session 2 - Tutor: Ballarin
07.09.20
01.10.20
Monday - Thursday
15:30
17:00
online


Course Type: core course

Course Number: E701

Credits: 8

Prerequisites

Mathematics for Economists, intermediate knowledge of microeconomics


Course Content

The course is aimed at doctoral students in all fields of business administration. It provides an introduction to microeconomic theory on a graduate level, highlighting aspects which are of specific relevance for business research.

The main topics covered include:

  1. Theory of consumer choice under certainty and uncertainty
  2. Theory of the firm, production cost and supply
  3. Markets, equilibrium, welfare
  4. Strategic behavior under complete and incomplete information
  5. Incentives and asymmetric information

The objective of the course is to enable doctoral students in business to read and discuss economic theory and to equip them with the tools to formulate their own research based on theoretically grounded hypotheses.

Learning outcomes: Understanding and critically evaluating the fundamental concepts of microeconomic theory, game theory and mechanism design; learning the relevant tools and underlying assumptions for economic analysis in ongoing research.

Form of assessment: Midterm and final exams: 80-100%, Exercises: Up to 20%


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.20
08.12.20
Tuesday
15:30
17:00
O 151 + ZOOM
15.10.20
10.12.20
Thursday
15:30
17:00
O 151 + ZOOM
Tutorial
Q&A
15.10.20
10.12.20
Thursday
13:45
15:15
O 151 + ZOOM
19.10.20
07.12.20
Monday
13:45
15:00
O 151 + ZOOM

Lecturer(s)


Course Type: core course

Course Number: E703

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.


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.20
08.12.20
Tuesday
10:15
11:45
O 151 or BWL-ZOOM 15
15.10.20
10.12.20
Thursday
10:15
11:45
O 151 or BWL-ZOOM 14
Tutorial
07.10.20
09.12.20
Wednesday
15:30
17:00
L 7, 3-5, room 358 or BWL-ZOOM 20
09.10.20
11.12.20
Friday
13:45
15:15
O 151 or BWL-ZOOM 10


Course Type: core course

Course Number: IS/OPM 910

Course Content

The course focuses on current research topics in the field of information systems and operations management. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The course introduces students to the variety of research methods that are currently popular in empirical and theoretical research.

Learning outcomes: Students will learn to follow-up with and discuss about current research topics in information systems and operations management. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Form of assessment: Oral participation.

Seminar Dates are announced here.



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
28.09.20
Monday
15:30
17:00
online


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
15.10.20
17.12.20
Thursday
12:00
13:30
SO 318 or BWL-ZOOM-09

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
  • 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.


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.09.20
09.10.20
Friday
08:30
18:45
BWL ZOOM-03

Lecturer(s)


Course Type: elective course

Credits: 6

Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


The course is also part of the TRR 266 Accounting for Transparency



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.09.20
Tuesday
10:00
17:00
ZOOM
17.09.20
Thursday
10:00
17:00
ZOOM
21.09.20
Monday
10:00
17:00
ZOOM

Lecturer(s)


Course Type: elective course

Course Number: OPM 801

Credits: 8

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
14.10.20
09.12.20
Wednesday
15:30
18:45
BWL-ZOOM 16

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



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
16.10.20
11.12.20
Friday
10:15
13:30
SO 322 or BWL-ZOOM-08

Lecturer(s)


Course Type: elective course

Course Number: OPM 918

Credits: 8

Course Content

This elective course aims at PhD students in Operations. The course is taught in a seminar-style format. Each student gives three presentations about one own research project based on a draft of a paper. The aim is to discuss and sharpen the contributions of that work. The presentations are structured similar to papers in that field:

1.     Models: Problem description, Model formulations, and contributions to scientific literature

2.     Methods: Analytical or algorithmic approaches

3.     Managerial Insights: Structured properties, data analysis, and numerical results

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 discussions to each single point.

Learning outcomes: Students will learn how to structure and discuss their own research results for a presentation and for 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.

Form of assessment: Presentations during the course (60%), active contribution to class discussion (15%), draft of paper (25%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.10.20
11.12.20
Friday
10:15
11:45
Zoom
23.10.20
Friday
09:30
12:00
O 226/228

Lecturer(s)


Course Type: elective course

Course Number: OPM 918

Credits: 8

Course Content

This elective course aims at PhD students in Operations. The course is taught in a seminar-style format. Each student gives three presentations about one own research project based on a draft of a paper. The aim is to discuss and sharpen the contributions of that work. The presentations are structured similar to papers in that field:

1.     Models: Problem description, Model formulations, and contributions to scientific literature

2.     Methods: Analytical or algorithmic approaches

3.     Managerial Insights: Structured properties, data analysis, and numerical results

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 discussions to each single point.

Learning outcomes: Students will learn how to structure and discuss their own research results for a presentation and for 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.

Form of assessment: Presentations during the course (60%), active contribution to class discussion (15%), draft of paper (25%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
02.10.20
11.12.20
Friday
10:15
11:45
Zoom
23.10.20
Friday
09:30
12:00
O 226/228

Lecturer(s)


Course Type: core course

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
01.10.20
10.12.20
Thursday
12:00
13:30
JURA-ZOOM-03


Course Type: core course

Course Number: ACC/TAX 910

Course Content

The course focuses on current research topics in the field of accounting and taxation. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. For each presentation, a separate preparation session for the Ph.D. students is offered in advance by rotating faculty. Overall, the course deepens the students’ insights into a variety of research methods that are currently popular in empirical and theoretical research.

Learning outcomes: Students will learn to follow-up with and discuss about current research topics in accounting and taxation. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Seminar Dates are announced here.



Course Type: core course

Course Number: ACC/TAX 920

Course Content

The course is taught in a seminar-style format. Students present their own research ideas at different stages of the project (early ideas, preliminary results, and complete working papers). The presentations involve an interactive discussion between faculty and students about the project’s potential contribution, related literature, research design and interpretation of results.

Learning outcomes: Students will learn how to present and discuss their own research results in a scientific format. They will become acquainted with acting as a discussant for other topics. Students will gain insights into the assessment of contribution, research design, and interpretation of research papers. The development of these skills is also helpful for writing scientific referee reports.


Coursedates will be announced via email to registered participants.


Lecturer(s)


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.

 

Teaching Assistants

Exercise Session 1
Tutor: Thi Anh Dam
Time: from 13.45 to 15:15
Supporter: Giovanni Ballarin

Exercise Session 2
Tutor: Giovanni Ballarin
Time: from 15:30 to 17:00
Supporter: Dam Thi Anh


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
07.09.20
01.10.20
Monday - Thursday
10:15
11:45
online
Exam
08.10.20
Thursday
08:00
10:00
001. A (A 3 Bibl.,Hörsaalgebäude)
Retake Exam
03.12.20
Thursday
14:00
16:00
O 148
Tutorial
Session 1 - Tutor: Dam
07.09.20
01.10.20
Monday - Thursday
13:45
15:15
online
Session 2 - Tutor: Ballarin
07.09.20
01.10.20
Monday - Thursday
15:30
17:00
online


Course Type: core course

Course Number: E701

Credits: 8

Prerequisites

Mathematics for Economists, intermediate knowledge of microeconomics


Course Content

The course is aimed at doctoral students in all fields of business administration. It provides an introduction to microeconomic theory on a graduate level, highlighting aspects which are of specific relevance for business research.

The main topics covered include:

  1. Theory of consumer choice under certainty and uncertainty
  2. Theory of the firm, production cost and supply
  3. Markets, equilibrium, welfare
  4. Strategic behavior under complete and incomplete information
  5. Incentives and asymmetric information

The objective of the course is to enable doctoral students in business to read and discuss economic theory and to equip them with the tools to formulate their own research based on theoretically grounded hypotheses.

Learning outcomes: Understanding and critically evaluating the fundamental concepts of microeconomic theory, game theory and mechanism design; learning the relevant tools and underlying assumptions for economic analysis in ongoing research.

Form of assessment: Midterm and final exams: 80-100%, Exercises: Up to 20%


The course is also part of the TRR 266 Accounting for Transparency


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.10.20
08.12.20
Tuesday
15:30
17:00
O 151 + ZOOM
15.10.20
10.12.20
Thursday
15:30
17:00
O 151 + ZOOM
Tutorial
Q&A
15.10.20
10.12.20
Thursday
13:45
15:15
O 151 + ZOOM
19.10.20
07.12.20
Monday
13:45
15:00
O 151 + ZOOM


Course Type: core course

Course Number: E702

Credits: 8

Prerequisites

E700


Course Content

Goals and Contents of the Module:

This course provides an introduction to the foundations of modern macroeconomic analysis. The main object of this course will be structural dynamic models where households' preference, firms' technology, and market structure are explicitly specified. The behaviors of agents in the model economy are derived based on microeconomic foundations. The macroeconomic aggregates are then determined by aggregating individuals' micro-founded decisions. We will consider some applications as well.


Requirements for the assignment of ECTS credits and grades:

  • Problem sets (15%)
  • Midterm (90 min, 35%)
  • Final exam (120 min, 50%)


Literature:

  • Stokey, Nancy, and Robert Lucas with Edward Prescott (1989): Recursive Methods in Economic Dynamics. Harvard University Press.
  • Ljungqvist, Lars, and Thomas J. Sargent. (2012) Recursive macroeconomic theory. MIT press.
  • Acemoglu, Daron (2009): Introduction to Modern Economic Growth, Princeton University Press.

    Competences acquired

    Expected Competences acquired after Completion of the Module:

    At the end of the semester, students are expected to be familiar with the basic methodology such as recursive methods and dynamic programming as well as the basic macroeconomic models.

     

    Teaching Assistant

    Alexander Matusche

     


    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    Lecture
    13.10.20
    08.12.20
    Tuesday
    15:30
    17:00
    online
    Lecture
    14.10.20
    09.12.20
    Wednesday
    15:30
    17:00
    online
    Exam
    17.12.20
    Thursday
    00:00
    00:00
    tba
    Retake exam
    28.01.21
    Thursday
    14:00
    16:00
    online
    Tutorial
    Tutorial
    13.10.20
    08.12.20
    Tuesday
    13:45
    15:15
    online
    Tutorial
    16.10.20
    11.12.20
    Friday
    10:15
    11:45
    online

    Lecturer(s)


    Course Type: core course

    Course Number: E703

    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.


    The course is also part of the TRR 266 Accounting for Transparency


    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    06.10.20
    08.12.20
    Tuesday
    10:15
    11:45
    O 151 or BWL-ZOOM 15
    15.10.20
    10.12.20
    Thursday
    10:15
    11:45
    O 151 or BWL-ZOOM 14
    Tutorial
    07.10.20
    09.12.20
    Wednesday
    15:30
    17:00
    L 7, 3-5, room 358 or BWL-ZOOM 20
    09.10.20
    11.12.20
    Friday
    13:45
    15:15
    O 151 or BWL-ZOOM 10

    Lecturer(s)


    Course Type: core 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%


    The course is also part of the TRR 266 Accounting for Transparency


    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    05.10.20
    07.12.20
    Monday
    08:30
    10:00
    O 048 or BWL-ZOOM-14
    07.10.20
    09.12.20
    Wednesday
    08:30
    10:00
    O 048 or BWL-ZOOM-14

    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
    • 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.


    The course is also part of the TRR 266 Accounting for Transparency


    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    11.09.20
    09.10.20
    Friday
    08:30
    18:45
    BWL ZOOM-03

    Lecturer(s)


    Course Type: elective course

    Credits: 6

    Course Content

    In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

    In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

    The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

    The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

    As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

    As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

    Students should install Phyton on their laptop before the course. An installation manual will be provided.

    Learning outcomes:

    • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
    • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
    • Students will obtain a solid programming knowledge in Python.

    Form of assessment: Assignment


    The course is also part of the TRR 266 Accounting for Transparency



    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    15.09.20
    Tuesday
    10:00
    17:00
    ZOOM
    17.09.20
    Thursday
    10:00
    17:00
    ZOOM
    21.09.20
    Monday
    10:00
    17:00
    ZOOM


    Course Type: elective course

    Course Number: TAX 923

    Credits: 5

    Course Content

    The course provides a forum to discuss recent state-of-the art papers in taxation research (mostly applied empirical). All covered papers are recently published or in the working paper stage. In each class session, one student briefly presents a research paper before the paper is discussed in class. All students are expected to read the research paper to be discussed in preparation for the class and it is one main objectives of the course that papers are lively discussed among all class participants.

    Students can choose papers which they wish to present or the responsible instructors provide a selection from which to pick. Students are encouraged to choose papers which are on the reading list for their thesis. The course could also serve as a forum for discussing paper drafts of peers or researchers within the network.

    In addition to presenting a paper in class, students are expected to write a referee report for a research paper. This will teach how to evaluate a paper critically and how to write a referee report.

    The reading course is particularly aimed at 2nd and higher year Ph.D. students to support them during their research phase. 1st year PhD students are welcomed to attend the class as well. Students can attend and earn credits for both this class as well as the related class TAX 922 (which is taught in the spring semester).

    Learning outcomes:

    • Know your field and related fields: Learn about the literature, both in your own (sub-field) of interest and other fields.
    • Commit to a reading routine for your thesis
    • Community building: The reading group will spawn discussion and encourage community building
    • Ability to present and confidence building: Learn how to present well. (This is often easier with a paper that somebody else wrote – one is not as emotionally involved in the question/ approach/ results as with one’s own paper.)
    • Discussion competence: Learn how to be a good seminar participant: Behave well, ask clear questions, discuss in an appropriate manner etc.
    • Ability to understand: Learn how to read and approach research papers and learn to summarize the main message/points of the paper
    • Participation in scientific discourse
    • Learn how to evaluate a paper critically
    • Writing a referee report

    Form of assessment: Paper (referee report) (40 %), Presentation (30 %), Class Participation (30 %)


    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    28.09.20
    07.12.20
    Monday
    10:15
    11:45
    BWL-ZOOM-12