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 2019


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: E 701

Credits: 8

Prerequisites

Mathematics for Economists, basic 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, 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. Equilibrium and 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 the foundation and formal derivation of the basic concepts of microeconomic theory, game theory and mechanism design.

Form of assessment: Written exams: Midterm (40%), final (60%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.10.19
03.12.19
Tuesday
17:15
18:45
O 135
10.10.19
05.12.19
Thursday
15:30
17:00
O 048

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
01.10.19
10.12.19
Tuesday
10:15
11:45
O 048
10.10.19
12.12.19
Thursday
10:15
11:45
O 135
Tutorial
09.10.19
04.12.19
Wednesday
12:00
13:30
L 7, 3-5, room 257
11.10.19
13.12.19
Friday
13:45
15:15
L 9, 1-2, room 009


Course Type: core course

Course Number: E700

Credits: 6

Prerequisites

Basic mathematical knowledge


Course Content

The course consists of four chapters:

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

Requirements for the assignment of ECTS Credits and Grades

Exam (120 min)

The exam takes place on October 2 2019, 3:30-5:30 pm in EW242 Otto Mann Hörsaal (Schloss Ehrenhof West).


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 Group 1 & 3: Can Çelebi (CDSE)

Exercise Group 2 & 4: Giovanni Ballarin (CDSE)

Download supplemental material >>

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
02.09.19
23.09.19
Monday
10:15
11:45
A5, 6, C015
Lecture
03.09.19
24.09.19
Tuesday
10:15
11:45
A5, 6, C015
Lecture
04.09.19
25.09.19
Wednesday
10:15
11:45
A5, 6, C014
Lecture
05.09.19
26.09.19
Thursday
10:15
11:45
B6, 30-32, E-F, 308
Written exam
02.10.19
Wednesday
15:30
17:30
EW242 Otto Mann Hörsaal (Schloss Ehrenhof West)
Retake Exam
11.12.19
Wednesday
15:30
17:30
A 5, 6, C 012
Tutorial
Group 1
02.09.19
23.09.19
Monday
13:45
15:15
B6, 23-25, A 304
Group 2
02.09.19
23.09.19
Monday
13:45
15:15
B6, 23-25, A 303
Group 3
02.09.19
23.09.19
Monday
15:30
17:00
B6, 23-25, A 304
Group 4
02.09.19
23.09.19
Monday
15:30
17:00
B6, 23-25, A 303
Group 1
03.09.19
24.09.19
Tuesday
13:45
15:15
B6, 23-25, A 304
Group 2
03.09.19
24.09.19
Tuesday
13:45
15:15
B6, 23-25, A 301
Group 3
03.09.19
24.09.19
Tuesday
15:30
17:00
B6, 23-25, A 304
Group 4
03.09.19
24.09.19
Tuesday
15:30
17:00
B6, 23-25, A 301
Group 1
04.09.19
25.09.19
Wednesday
13:45
15:15
B6, 23-25, A 304
Group 2
04.09.19
25.09.19
Wednesday
13:45
15:15
B6, 23-25, A 303
Group 3
04.09.19
25.09.19
Wednesday
15:30
17:00
B6, 23-25, A 304
Group 4
04.09.19
25.09.19
Wednesday
15:30
17:00
B6, 23-25, A 305
Group 1
05.09.19
26.09.19
Thursday
13:45
15:15
B6, 23-25, A 301
Group 2
05.09.19
26.09.19
Thursday
13:45
15:15
B6, 23-25, A 302
Group 3
05.09.19
26.09.19
Thursday
15:30
17:00
B6, 23-25, A 304
Group 4
05.09.19
26.09.19
Thursday
15:30
17:00
B6, 23-25, A 302

Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites

Basic programming skills (preferably in Python); basic knowledge of linear algebra (vector and matrix operations) and mathematical analysis (differentiation, gradients).


Course Content

  • Basics of machine learning: inductive bias; three ML components: model, loss function, and optimization algorithm; training, validation, and testing of ML models; model complexity and model optimization, underfitting and overfitting; ML paradigms: supervised, unsupervised, and reinforcement learning. Evaluation of ML models.
  • Supervised machine learning: traditional (generative and discriminative) ML models – Naïve Bayes, Logistic regression, Support Vector Machines, non-parametric models (Decision Trees/Random Forests, K Nearest Neighbours).
  • Unsupervised machine learning: Clustering and outlier detection algorithms; single-pass clustering, K-means, Gaussian mixture with Expectation Maximization; graph clustering and minimum cut trees. 
  • Deep Learning and Representation Learning: feature-based ML vs. deep learning; neural networks and the backpropagation algorithm;unsupervised deep learning: autoencoders; supervised DL architectures: convolutional, recurrent, and attention-based networks.           

Learning outcomes:

  • Basic understanding of core ML concepts: learning theory, ML paradigms, model optimization and evaluation
  • Good understanding of a range supervised and unsupervised ML algorithms
  • Basic knowledge of the deep learning paradigm and common neural models
  • Familiarity with the ML libraries (e.g., Scikit-learn, Keras)
  • Ability to implement supervised and unsupervised ML models to solve real-world problems (e.g., text or image classification, regression from numeric features, etc.)

Form of assessment: Oral exam (15 minutes per student), 30 %; Individual Assignment 70 %.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
23.09.19
Monday
08:30
15:00
B 6, 30-32, room 211
07.10.19
Monday
08:30
15:00
B 6, 30-32, room 211
21.10.19
Monday
08:30
15:00
B 6, 30-32, room 211

Lecturer(s)


Course Type: elective 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 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.09.19
06.12.19
Friday
13:45
17:00
O 326/28

Lecturer(s)


Course Type: core course

Course Number: E 701

Credits: 8

Prerequisites

Mathematics for Economists, basic 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, 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. Equilibrium and 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 the foundation and formal derivation of the basic concepts of microeconomic theory, game theory and mechanism design.

Form of assessment: Written exams: Midterm (40%), final (60%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.10.19
03.12.19
Tuesday
17:15
18:45
O 135
10.10.19
05.12.19
Thursday
15:30
17:00
O 048

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
01.10.19
10.12.19
Tuesday
10:15
11:45
O 048
10.10.19
12.12.19
Thursday
10:15
11:45
O 135
Tutorial
09.10.19
04.12.19
Wednesday
12:00
13:30
L 7, 3-5, room 257
11.10.19
13.12.19
Friday
13:45
15:15
L 9, 1-2, room 009


Course Type: core course

Course Number: E700

Credits: 6

Prerequisites

Basic mathematical knowledge


Course Content

The course consists of four chapters:

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

Requirements for the assignment of ECTS Credits and Grades

Exam (120 min)

The exam takes place on October 2 2019, 3:30-5:30 pm in EW242 Otto Mann Hörsaal (Schloss Ehrenhof West).


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 Group 1 & 3: Can Çelebi (CDSE)

Exercise Group 2 & 4: Giovanni Ballarin (CDSE)

Download supplemental material >>

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
02.09.19
23.09.19
Monday
10:15
11:45
A5, 6, C015
Lecture
03.09.19
24.09.19
Tuesday
10:15
11:45
A5, 6, C015
Lecture
04.09.19
25.09.19
Wednesday
10:15
11:45
A5, 6, C014
Lecture
05.09.19
26.09.19
Thursday
10:15
11:45
B6, 30-32, E-F, 308
Written exam
02.10.19
Wednesday
15:30
17:30
EW242 Otto Mann Hörsaal (Schloss Ehrenhof West)
Retake Exam
11.12.19
Wednesday
15:30
17:30
A 5, 6, C 012
Tutorial
Group 1
02.09.19
23.09.19
Monday
13:45
15:15
B6, 23-25, A 304
Group 2
02.09.19
23.09.19
Monday
13:45
15:15
B6, 23-25, A 303
Group 3
02.09.19
23.09.19
Monday
15:30
17:00
B6, 23-25, A 304
Group 4
02.09.19
23.09.19
Monday
15:30
17:00
B6, 23-25, A 303
Group 1
03.09.19
24.09.19
Tuesday
13:45
15:15
B6, 23-25, A 304
Group 2
03.09.19
24.09.19
Tuesday
13:45
15:15
B6, 23-25, A 301
Group 3
03.09.19
24.09.19
Tuesday
15:30
17:00
B6, 23-25, A 304
Group 4
03.09.19
24.09.19
Tuesday
15:30
17:00
B6, 23-25, A 301
Group 1
04.09.19
25.09.19
Wednesday
13:45
15:15
B6, 23-25, A 304
Group 2
04.09.19
25.09.19
Wednesday
13:45
15:15
B6, 23-25, A 303
Group 3
04.09.19
25.09.19
Wednesday
15:30
17:00
B6, 23-25, A 304
Group 4
04.09.19
25.09.19
Wednesday
15:30
17:00
B6, 23-25, A 305
Group 1
05.09.19
26.09.19
Thursday
13:45
15:15
B6, 23-25, A 301
Group 2
05.09.19
26.09.19
Thursday
13:45
15:15
B6, 23-25, A 302
Group 3
05.09.19
26.09.19
Thursday
15:30
17:00
B6, 23-25, A 304
Group 4
05.09.19
26.09.19
Thursday
15:30
17:00
B6, 23-25, A 302

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
16.10.19
06.11.19
Wednesday
09:00
12:30
L 9, 1-2, room 409
18.10.19
Friday
09:00
18:00
L 9, 1-2, room 409
Written Exam
13.11.19
Wednesday
10:30
12:30
L 9, 1-2, room 409
Presentation of Term Papers
04.12.19
Wednesday
09:30
12:30
L 9, 1-2, room 409

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 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

Prerequisites

Basic programming skills (preferably in Python); basic knowledge of linear algebra (vector and matrix operations) and mathematical analysis (differentiation, gradients).


Course Content

  • Basics of machine learning: inductive bias; three ML components: model, loss function, and optimization algorithm; training, validation, and testing of ML models; model complexity and model optimization, underfitting and overfitting; ML paradigms: supervised, unsupervised, and reinforcement learning. Evaluation of ML models.
  • Supervised machine learning: traditional (generative and discriminative) ML models – Naïve Bayes, Logistic regression, Support Vector Machines, non-parametric models (Decision Trees/Random Forests, K Nearest Neighbours).
  • Unsupervised machine learning: Clustering and outlier detection algorithms; single-pass clustering, K-means, Gaussian mixture with Expectation Maximization; graph clustering and minimum cut trees. 
  • Deep Learning and Representation Learning: feature-based ML vs. deep learning; neural networks and the backpropagation algorithm;unsupervised deep learning: autoencoders; supervised DL architectures: convolutional, recurrent, and attention-based networks.           

Learning outcomes:

  • Basic understanding of core ML concepts: learning theory, ML paradigms, model optimization and evaluation
  • Good understanding of a range supervised and unsupervised ML algorithms
  • Basic knowledge of the deep learning paradigm and common neural models
  • Familiarity with the ML libraries (e.g., Scikit-learn, Keras)
  • Ability to implement supervised and unsupervised ML models to solve real-world problems (e.g., text or image classification, regression from numeric features, etc.)

Form of assessment: Oral exam (15 minutes per student), 30 %; Individual Assignment 70 %.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
23.09.19
Monday
08:30
15:00
B 6, 30-32, room 211
07.10.19
Monday
08:30
15:00
B 6, 30-32, room 211
21.10.19
Monday
08:30
15:00
B 6, 30-32, room 211


Course Type: elective course

Course Number: FIN 913

Credits: 4+4

Prerequisites

Recommended: The course requires some knowledge of probability theory and statistics. Furthermore, students should be familiar with the pricing of standard financial instruments including derivatives. 


Course Content

This course includes FIN 660 (4 ECTS), which covers tail risk measures, market and credit risk, risk aggregation as well as portfolio risk decomposition. The additional sessions for FIN 913 (4 ECTS) extend the material on selected topics such as the theoretical properties of modern tail risk measures, flexible parametric and semiparametric tail risk estimators, dynamic risk forecasting as well as backtesting. Furthermore, advanced topics in market and credit risk including credit derivatives will be covered.

Learning outcomes: Students understand modern tail risk measures and are able to discuss their strengths and weaknesses. They are familiar with advanced techniques for tail risk estimation including time-series methods, extreme-value theory and copulas. Students understand alternatives to the variance-covariance approach for market risk and are familiar with important extensions of basic credit risk models. In addition, they obtain an advanced understanding for the pricing of risky debt and credit derivatives.

Form of assessment: Written Exam (90 minutes) 80%, Solving a Case Study 10%, Class Participation 10%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
FIN 660
03.09.19
03.12.19
Tuesday
12:00
13:00
O 148
FIN 913 Kick-Off
05.09.19
Thursday
12:00
13:30
O 328

Lecturer(s)


Course Type: elective course

Course Number: FIN 922

Credits: 4

Prerequisites

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

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.


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
02.09.19
Monday
10:15
13:15
L 9, 1-2, room 002
16.09.19
Monday
10:15
13:15
L 9, 1-2, room 002
30.09.19
Monday
10:15
13:15
L 9, 1-2, room 002
14.10.19
Monday
10:15
13:15
L 9, 1-2, room 002
28.10.19
Monday
10:15
13:15
L 9, 1-2, room 002
11.11.19
Monday
10:15
13:15
L 9, 1-2, room 002

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
Kick-off
09.09.19
Monday
17:15
20:30


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

Prerequisites

Basic programming skills (preferably in Python); basic knowledge of linear algebra (vector and matrix operations) and mathematical analysis (differentiation, gradients).


Course Content

  • Basics of machine learning: inductive bias; three ML components: model, loss function, and optimization algorithm; training, validation, and testing of ML models; model complexity and model optimization, underfitting and overfitting; ML paradigms: supervised, unsupervised, and reinforcement learning. Evaluation of ML models.
  • Supervised machine learning: traditional (generative and discriminative) ML models – Naïve Bayes, Logistic regression, Support Vector Machines, non-parametric models (Decision Trees/Random Forests, K Nearest Neighbours).
  • Unsupervised machine learning: Clustering and outlier detection algorithms; single-pass clustering, K-means, Gaussian mixture with Expectation Maximization; graph clustering and minimum cut trees. 
  • Deep Learning and Representation Learning: feature-based ML vs. deep learning; neural networks and the backpropagation algorithm;unsupervised deep learning: autoencoders; supervised DL architectures: convolutional, recurrent, and attention-based networks.           

Learning outcomes:

  • Basic understanding of core ML concepts: learning theory, ML paradigms, model optimization and evaluation
  • Good understanding of a range supervised and unsupervised ML algorithms
  • Basic knowledge of the deep learning paradigm and common neural models
  • Familiarity with the ML libraries (e.g., Scikit-learn, Keras)
  • Ability to implement supervised and unsupervised ML models to solve real-world problems (e.g., text or image classification, regression from numeric features, etc.)

Form of assessment: Oral exam (15 minutes per student), 30 %; Individual Assignment 70 %.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
23.09.19
Monday
08:30
15:00
B 6, 30-32, room 211
07.10.19
Monday
08:30
15:00
B 6, 30-32, room 211
21.10.19
Monday
08:30
15:00
B 6, 30-32, room 211

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
09.09.19
Monday
14:30
16:30
L 5, 4, room 207-209
14.10.19
Monday
14:30
16:30
L 5, 4, room 207-209
18.11.19
Monday
09:00
17:30
L 5, 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 (20 minutes) 75 %, presentation 25 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
11.10.19
25.10.19
Friday
09:00
17:00
SO 422

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
17.09.19
Tuesday
14:00
17:00
L 9, 1-2, room 210
22.10.19
Tuesday
09:00
18:00
L 9, 1-2, room 409
29.10.19
Tuesday
09:00
18:00
L 9, 1-2, room 210


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: 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
12.11.19
Tuesday
09:30
12:00
L 5, 2 – room 107
13.11.19
Wednesday
09:30
12:00
L 5, 2 – room 107
15.11.19
Friday
09:30
17:00
L 5, 1 – room 009
27.11.19
Wednesday
09:30
17:00
L 5, 1 – room 009
29.11.19
Friday
09:30
17:00
L 5, 1 – room 009

Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites

Basic programming skills (preferably in Python); basic knowledge of linear algebra (vector and matrix operations) and mathematical analysis (differentiation, gradients).


Course Content

  • Basics of machine learning: inductive bias; three ML components: model, loss function, and optimization algorithm; training, validation, and testing of ML models; model complexity and model optimization, underfitting and overfitting; ML paradigms: supervised, unsupervised, and reinforcement learning. Evaluation of ML models.
  • Supervised machine learning: traditional (generative and discriminative) ML models – Naïve Bayes, Logistic regression, Support Vector Machines, non-parametric models (Decision Trees/Random Forests, K Nearest Neighbours).
  • Unsupervised machine learning: Clustering and outlier detection algorithms; single-pass clustering, K-means, Gaussian mixture with Expectation Maximization; graph clustering and minimum cut trees. 
  • Deep Learning and Representation Learning: feature-based ML vs. deep learning; neural networks and the backpropagation algorithm;unsupervised deep learning: autoencoders; supervised DL architectures: convolutional, recurrent, and attention-based networks.           

Learning outcomes:

  • Basic understanding of core ML concepts: learning theory, ML paradigms, model optimization and evaluation
  • Good understanding of a range supervised and unsupervised ML algorithms
  • Basic knowledge of the deep learning paradigm and common neural models
  • Familiarity with the ML libraries (e.g., Scikit-learn, Keras)
  • Ability to implement supervised and unsupervised ML models to solve real-world problems (e.g., text or image classification, regression from numeric features, etc.)

Form of assessment: Oral exam (15 minutes per student), 30 %; Individual Assignment 70 %.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
23.09.19
Monday
08:30
15:00
B 6, 30-32, room 211
07.10.19
Monday
08:30
15:00
B 6, 30-32, room 211
21.10.19
Monday
08:30
15:00
B 6, 30-32, room 211


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
09.10.19
Wednesday
10:00
13:00
L 9, 1-2 room 210
12.11.19
Tuesday
10:00
17:00
L 9, 1-2 room 210

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
01.10.19
10.12.19
Tuesday
10:15
11:45
O 048
10.10.19
12.12.19
Thursday
10:15
11:45
O 135
Tutorial
09.10.19
04.12.19
Wednesday
12:00
13:30
L 7, 3-5, room 257
11.10.19
13.12.19
Friday
13:45
15:15
L 9, 1-2, room 009

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
06.09.19
06.12.19
Friday
10:15
13:30
L5, 2, room 107

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
12.11.19
Tuesday
09:30
12:00
L 5, 2 – room 107
13.11.19
Wednesday
09:30
12:00
L 5, 2 – room 107
15.11.19
Friday
09:30
17:00
L 5, 1 – room 009
27.11.19
Wednesday
09:30
17:00
L 5, 1 – room 009
29.11.19
Friday
09:30
17:00
L 5, 1 – room 009

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

Credits: 6

Prerequisites

Basic programming skills (preferably in Python); basic knowledge of linear algebra (vector and matrix operations) and mathematical analysis (differentiation, gradients).


Course Content

  • Basics of machine learning: inductive bias; three ML components: model, loss function, and optimization algorithm; training, validation, and testing of ML models; model complexity and model optimization, underfitting and overfitting; ML paradigms: supervised, unsupervised, and reinforcement learning. Evaluation of ML models.
  • Supervised machine learning: traditional (generative and discriminative) ML models – Naïve Bayes, Logistic regression, Support Vector Machines, non-parametric models (Decision Trees/Random Forests, K Nearest Neighbours).
  • Unsupervised machine learning: Clustering and outlier detection algorithms; single-pass clustering, K-means, Gaussian mixture with Expectation Maximization; graph clustering and minimum cut trees. 
  • Deep Learning and Representation Learning: feature-based ML vs. deep learning; neural networks and the backpropagation algorithm;unsupervised deep learning: autoencoders; supervised DL architectures: convolutional, recurrent, and attention-based networks.           

Learning outcomes:

  • Basic understanding of core ML concepts: learning theory, ML paradigms, model optimization and evaluation
  • Good understanding of a range supervised and unsupervised ML algorithms
  • Basic knowledge of the deep learning paradigm and common neural models
  • Familiarity with the ML libraries (e.g., Scikit-learn, Keras)
  • Ability to implement supervised and unsupervised ML models to solve real-world problems (e.g., text or image classification, regression from numeric features, etc.)

Form of assessment: Oral exam (15 minutes per student), 30 %; Individual Assignment 70 %.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
23.09.19
Monday
08:30
15:00
B 6, 30-32, room 211
07.10.19
Monday
08:30
15:00
B 6, 30-32, room 211
21.10.19
Monday
08:30
15:00
B 6, 30-32, room 211

Lecturer(s)


Course Type: elective course

Course Number: MKT 912

Credits: 8

Course Content

In this course students will write a publishable paper before January 1, 2020. Arch Woodside will provide and discuss four sources of data and discuss the use of these data in the first group session; additional sources of data from students may be available for use to meet the course requirements. To this end, the course will consist of one-to-one meetings between each student team and the lecturer (A.G.Woodside), as well as group sessions for discussions and presentations. One-to-one meetings are intended to work and elaborate on critical issues regarding data analysis, paper positioning, and scientific writing. Group sessions are intended to provide students the opportunity to go beyond the scope of their own research project to familiarize with and learn from other students’ projects during the course.

There is a set of mandatory readings that must be prepared/read prior to class that the reading is assigned. Each student will provide a summary and critique of two readings during the group sessions. Each summary/critique should include 10 to 15 PowerPoint slides; the slides should cover theory, literature review, discussion of analysis, findings, conclusions, evaluation of the contribution that the article makes; and a suggestion for additional research that relates to the article.

Learning outcomes: At the end of this course, students will be able to transform a research project into a publishable paper. This course will facilitate students’ ability to conduct sound academic research and will improve students’ scientific paper writing skills.

Form of assessment: Presentations (40 %): includes 20 % for each of two in class article/chapter presentations; Paper (60 %): includes 20% for data analysis and 40% for the quality and completeness of the written paper


Material


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
16.09.19
07.10.19
Monday
08:30
12:30
L 5, 1, Roche Forum
18.09.19
09.10.19
Wednesday
08:30
12:30
L 5, 1, Roche Forum

Lecturer(s)


Course Type: core course

Course Number: E 701

Credits: 8

Prerequisites

Mathematics for Economists, basic 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, 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. Equilibrium and 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 the foundation and formal derivation of the basic concepts of microeconomic theory, game theory and mechanism design.

Form of assessment: Written exams: Midterm (40%), final (60%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.10.19
03.12.19
Tuesday
17:15
18:45
O 135
10.10.19
05.12.19
Thursday
15:30
17:00
O 048

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
01.10.19
10.12.19
Tuesday
10:15
11:45
O 048
10.10.19
12.12.19
Thursday
10:15
11:45
O 135
Tutorial
09.10.19
04.12.19
Wednesday
12:00
13:30
L 7, 3-5, room 257
11.10.19
13.12.19
Friday
13:45
15:15
L 9, 1-2, room 009


Course Type: core course

Course Number: E700

Credits: 6

Prerequisites

Basic mathematical knowledge


Course Content

The course consists of four chapters:

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

Requirements for the assignment of ECTS Credits and Grades

Exam (120 min)

The exam takes place on October 2 2019, 3:30-5:30 pm in EW242 Otto Mann Hörsaal (Schloss Ehrenhof West).


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 Group 1 & 3: Can Çelebi (CDSE)

Exercise Group 2 & 4: Giovanni Ballarin (CDSE)

Download supplemental material >>

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
02.09.19
23.09.19
Monday
10:15
11:45
A5, 6, C015
Lecture
03.09.19
24.09.19
Tuesday
10:15
11:45
A5, 6, C015
Lecture
04.09.19
25.09.19
Wednesday
10:15
11:45
A5, 6, C014
Lecture
05.09.19
26.09.19
Thursday
10:15
11:45
B6, 30-32, E-F, 308
Written exam
02.10.19
Wednesday
15:30
17:30
EW242 Otto Mann Hörsaal (Schloss Ehrenhof West)
Retake Exam
11.12.19
Wednesday
15:30
17:30
A 5, 6, C 012
Tutorial
Group 1
02.09.19
23.09.19
Monday
13:45
15:15
B6, 23-25, A 304
Group 2
02.09.19
23.09.19
Monday
13:45
15:15
B6, 23-25, A 303
Group 3
02.09.19
23.09.19
Monday
15:30
17:00
B6, 23-25, A 304
Group 4
02.09.19
23.09.19
Monday
15:30
17:00
B6, 23-25, A 303
Group 1
03.09.19
24.09.19
Tuesday
13:45
15:15
B6, 23-25, A 304
Group 2
03.09.19
24.09.19
Tuesday
13:45
15:15
B6, 23-25, A 301
Group 3
03.09.19
24.09.19
Tuesday
15:30
17:00
B6, 23-25, A 304
Group 4
03.09.19
24.09.19
Tuesday
15:30
17:00
B6, 23-25, A 301
Group 1
04.09.19
25.09.19
Wednesday
13:45
15:15
B6, 23-25, A 304
Group 2
04.09.19
25.09.19
Wednesday
13:45
15:15
B6, 23-25, A 303
Group 3
04.09.19
25.09.19
Wednesday
15:30
17:00
B6, 23-25, A 304
Group 4
04.09.19
25.09.19
Wednesday
15:30
17:00
B6, 23-25, A 305
Group 1
05.09.19
26.09.19
Thursday
13:45
15:15
B6, 23-25, A 301
Group 2
05.09.19
26.09.19
Thursday
13:45
15:15
B6, 23-25, A 302
Group 3
05.09.19
26.09.19
Thursday
15:30
17:00
B6, 23-25, A 304
Group 4
05.09.19
26.09.19
Thursday
15:30
17:00
B6, 23-25, A 302


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
09.09.19
Monday
10:15
11:45
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
05.09.19
05.12.19
Thursday
12:00
13:30
SO 318

Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites

Basic programming skills (preferably in Python); basic knowledge of linear algebra (vector and matrix operations) and mathematical analysis (differentiation, gradients).


Course Content

  • Basics of machine learning: inductive bias; three ML components: model, loss function, and optimization algorithm; training, validation, and testing of ML models; model complexity and model optimization, underfitting and overfitting; ML paradigms: supervised, unsupervised, and reinforcement learning. Evaluation of ML models.
  • Supervised machine learning: traditional (generative and discriminative) ML models – Naïve Bayes, Logistic regression, Support Vector Machines, non-parametric models (Decision Trees/Random Forests, K Nearest Neighbours).
  • Unsupervised machine learning: Clustering and outlier detection algorithms; single-pass clustering, K-means, Gaussian mixture with Expectation Maximization; graph clustering and minimum cut trees. 
  • Deep Learning and Representation Learning: feature-based ML vs. deep learning; neural networks and the backpropagation algorithm;unsupervised deep learning: autoencoders; supervised DL architectures: convolutional, recurrent, and attention-based networks.           

Learning outcomes:

  • Basic understanding of core ML concepts: learning theory, ML paradigms, model optimization and evaluation
  • Good understanding of a range supervised and unsupervised ML algorithms
  • Basic knowledge of the deep learning paradigm and common neural models
  • Familiarity with the ML libraries (e.g., Scikit-learn, Keras)
  • Ability to implement supervised and unsupervised ML models to solve real-world problems (e.g., text or image classification, regression from numeric features, etc.)

Form of assessment: Oral exam (15 minutes per student), 30 %; Individual Assignment 70 %.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
23.09.19
Monday
08:30
15:00
B 6, 30-32, room 211
07.10.19
Monday
08:30
15:00
B 6, 30-32, room 211
21.10.19
Monday
08:30
15:00
B 6, 30-32, room 211

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
04.10.19
06.12.19
Friday
10:15
13:30
SO 322

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
05.09.19
05.12.19
Thursday
12:00
13:30
W 114


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: E 701

Credits: 8

Prerequisites

Mathematics for Economists, basic 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, 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. Equilibrium and 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 the foundation and formal derivation of the basic concepts of microeconomic theory, game theory and mechanism design.

Form of assessment: Written exams: Midterm (40%), final (60%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.10.19
03.12.19
Tuesday
17:15
18:45
O 135
10.10.19
05.12.19
Thursday
15:30
17:00
O 048

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
01.10.19
10.12.19
Tuesday
10:15
11:45
O 048
10.10.19
12.12.19
Thursday
10:15
11:45
O 135
Tutorial
09.10.19
04.12.19
Wednesday
12:00
13:30
L 7, 3-5, room 257
11.10.19
13.12.19
Friday
13:45
15:15
L 9, 1-2, room 009


Course Type: core course

Course Number: E700

Credits: 6

Prerequisites

Basic mathematical knowledge


Course Content

The course consists of four chapters:

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

Requirements for the assignment of ECTS Credits and Grades

Exam (120 min)

The exam takes place on October 2 2019, 3:30-5:30 pm in EW242 Otto Mann Hörsaal (Schloss Ehrenhof West).


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 Group 1 & 3: Can Çelebi (CDSE)

Exercise Group 2 & 4: Giovanni Ballarin (CDSE)

Download supplemental material >>

 


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Lecture
02.09.19
23.09.19
Monday
10:15
11:45
A5, 6, C015
Lecture
03.09.19
24.09.19
Tuesday
10:15
11:45
A5, 6, C015
Lecture
04.09.19
25.09.19
Wednesday
10:15
11:45
A5, 6, C014
Lecture
05.09.19
26.09.19
Thursday
10:15
11:45
B6, 30-32, E-F, 308
Written exam
02.10.19
Wednesday
15:30
17:30
EW242 Otto Mann Hörsaal (Schloss Ehrenhof West)
Retake Exam
11.12.19
Wednesday
15:30
17:30
A 5, 6, C 012
Tutorial
Group 1
02.09.19
23.09.19
Monday
13:45
15:15
B6, 23-25, A 304
Group 2
02.09.19
23.09.19
Monday
13:45
15:15
B6, 23-25, A 303
Group 3
02.09.19
23.09.19
Monday
15:30
17:00
B6, 23-25, A 304
Group 4
02.09.19
23.09.19
Monday
15:30
17:00
B6, 23-25, A 303
Group 1
03.09.19
24.09.19
Tuesday
13:45
15:15
B6, 23-25, A 304
Group 2
03.09.19
24.09.19
Tuesday
13:45
15:15
B6, 23-25, A 301
Group 3
03.09.19
24.09.19
Tuesday
15:30
17:00
B6, 23-25, A 304
Group 4
03.09.19
24.09.19
Tuesday
15:30
17:00
B6, 23-25, A 301
Group 1
04.09.19
25.09.19
Wednesday
13:45
15:15
B6, 23-25, A 304
Group 2
04.09.19
25.09.19
Wednesday
13:45
15:15
B6, 23-25, A 303
Group 3
04.09.19
25.09.19
Wednesday
15:30
17:00
B6, 23-25, A 304
Group 4
04.09.19
25.09.19
Wednesday
15:30
17:00
B6, 23-25, A 305
Group 1
05.09.19
26.09.19
Thursday
13:45
15:15
B6, 23-25, A 301
Group 2
05.09.19
26.09.19
Thursday
13:45
15:15
B6, 23-25, A 302
Group 3
05.09.19
26.09.19
Thursday
15:30
17:00
B6, 23-25, A 304
Group 4
05.09.19
26.09.19
Thursday
15:30
17:00
B6, 23-25, A 302


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

    Timo Reinelt

     


    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    Lecture
    08.10.19
    03.12.19
    Tuesday
    15:30
    17:00
    L7, 3-5, S031
    Lecture
    09.10.19
    04.12.19
    Wednesday
    15:30
    17:00
    L7, 3-5, P044
    Written Exam
    12.12.19
    Thursday
    10:15
    12:15
    Schloss O 129
    Retake exam
    30.12.19
    Thursday
    10:15
    12:15
    B6, 30-32, 211
    Tutorial
    Tutorial 1
    09.10.19
    04.12.19
    Wednesday
    13:45
    15:15
    L7, 3-5, P 043
    Tutorial 2
    10.10.19
    05.12.19
    Thursday
    13:45
    15:15
    L7, 3-5, P 043

    Lecturer(s)


    Course Type: elective course

    Credits: 6

    Prerequisites

    Basic programming skills (preferably in Python); basic knowledge of linear algebra (vector and matrix operations) and mathematical analysis (differentiation, gradients).


    Course Content

    • Basics of machine learning: inductive bias; three ML components: model, loss function, and optimization algorithm; training, validation, and testing of ML models; model complexity and model optimization, underfitting and overfitting; ML paradigms: supervised, unsupervised, and reinforcement learning. Evaluation of ML models.
    • Supervised machine learning: traditional (generative and discriminative) ML models – Naïve Bayes, Logistic regression, Support Vector Machines, non-parametric models (Decision Trees/Random Forests, K Nearest Neighbours).
    • Unsupervised machine learning: Clustering and outlier detection algorithms; single-pass clustering, K-means, Gaussian mixture with Expectation Maximization; graph clustering and minimum cut trees. 
    • Deep Learning and Representation Learning: feature-based ML vs. deep learning; neural networks and the backpropagation algorithm;unsupervised deep learning: autoencoders; supervised DL architectures: convolutional, recurrent, and attention-based networks.           

    Learning outcomes:

    • Basic understanding of core ML concepts: learning theory, ML paradigms, model optimization and evaluation
    • Good understanding of a range supervised and unsupervised ML algorithms
    • Basic knowledge of the deep learning paradigm and common neural models
    • Familiarity with the ML libraries (e.g., Scikit-learn, Keras)
    • Ability to implement supervised and unsupervised ML models to solve real-world problems (e.g., text or image classification, regression from numeric features, etc.)

    Form of assessment: Oral exam (15 minutes per student), 30 %; Individual Assignment 70 %.


    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    23.09.19
    Monday
    08:30
    15:00
    B 6, 30-32, room 211
    07.10.19
    Monday
    08:30
    15:00
    B 6, 30-32, room 211
    21.10.19
    Monday
    08:30
    15:00
    B 6, 30-32, room 211

    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.



    Schedule

    Type
    From
    To
    Weekday
    From
    To
    Room
    Material
    Lecture
    13.09.19
    04.10.19
    Friday
    09:00
    18:00
    O 226/28

    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
    02.09.19
    09.12.19
    Monday
    08:30
    10:00
    O 226/28
    04.09.19
    11.12.19
    Wednesday
    08:30
    10:00
    L 9, 1-2, room 009

    Register

    Business Fall 2019

    ACC/TAX 910
    Area Seminar Accounting and Taxation
    ACC/TAX 920
    Brown Bag Seminar
    E 701
    Advanced Microeconomics I
    E 703
    Advanced Econometrics I
    E700
    Mathematics for Ecomomists
    Machine Learning
    ACC 905
    Applied Methods & Tools in Empirical Accounting Research (Paper Replication)
    FIN 801
    Discrete Time Finance
    FIN 910
    Area Seminar Finance
    FIN 913
    Advanced Quantitative Risk Management
    FIN 922
    Markets and Society: From Milton Friedman to Samuel Bowles
    IS 801
    Fundamentals of Design Science Research
    IS/OPM 910
    Area Seminar Information Systems & Operations Management
    MAN 802
    Fundamentals of Non-Profit Management Science
    MAN 805
    Applied Methods in Management Research
    MAN 806
    Advances in Organization and Innovation Research
    MAN 910
    Area Seminar Management
    MKT 903
    Advanced Business Econometrics
    MAN 809
    Theory Construction in the Social Sciences
    MKT 801
    Fundamentals of Marketing Research
    MKT 910
    Area Seminar Marketing
    MKT 912
    Performing Research and Publishing in Marketing
    OPM 805
    Research Seminar Business Analytics
    OPM 901
    Research Seminar Operations Management & Operations Research
    OPM 803
    Selected Topics in Nonlinear Optimization
    European Tax Law
    E702
    Advanced Macroeconomics I
    Statistics Refresher
    TAX 916
    Applied Econometrics I