Credit: Anna Logue
Fall 2019
-
Accounting
ACC/TAX 910: Area Seminar Accounting and Taxation ECTSLecturer(s)
Course Type: core courseCourse 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.
ACC/TAX 920: Brown Bag Seminar ECTSLecturer(s)
Course Type: core courseCourse 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.
E 701: Advanced Microeconomics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 701Credits: 8Prerequisites
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:
- Theory of consumer choice under certainty and uncertainty
- Theory of the firm, production cost and supply
- Equilibrium and welfare
- Strategic behavior under complete and incomplete information
- 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
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 E 703: Advanced Econometrics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 703Credits: 8Course 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
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 E700: Mathematics for Ecomomists6 ECTSLecturer(s)
Course Type: core courseCourse Number: E700Credits: 6Prerequisites
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
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 Machine Learning6 ECTSLecturer(s)
Course Type: elective courseCredits: 6Prerequisites
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
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 ACC 905: Applied Methods & Tools in Empirical Accounting Research (Paper Replication)8 ECTSLecturer(s)
Course Type: elective courseCourse Number: ACC 905Credits: 8Course 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
Lecture 06.09.19 – 06.12.19 Friday 13:45 – 17:00 O 326/ 28 -
Finance
E 701: Advanced Microeconomics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 701Credits: 8Prerequisites
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:
- Theory of consumer choice under certainty and uncertainty
- Theory of the firm, production cost and supply
- Equilibrium and welfare
- Strategic behavior under complete and incomplete information
- 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
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 E 703: Advanced Econometrics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 703Credits: 8Course 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
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 E700: Mathematics for Ecomomists6 ECTSLecturer(s)
Course Type: core courseCourse Number: E700Credits: 6Prerequisites
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
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 FIN 801: Discrete Time Finance8 ECTSLecturer(s)
Course Type: core courseCourse Number: FIN 801Credits: 8Prerequisites
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
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 FIN 910: Area Seminar FinanceECTSLecturer(s)
Course Type: core courseCourse Number: FIN 910Course 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.
Machine Learning6 ECTSLecturer(s)
Course Type: elective courseCredits: 6Prerequisites
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
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 FIN 913: Advanced Quantitative Risk Management4+4 ECTSLecturer(s)
Course Type: elective courseCourse Number: FIN 913Credits: 4+4Prerequisites
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
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 FIN 922: Markets and Society: From Milton Friedman to Samuel Bowles4 ECTSLecturer(s)
Course Type: elective courseCourse Number: FIN 922Credits: 4Prerequisites
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’ The Moral 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
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 -
Information Systems
IS 801: Fundamentals of Design Science Research8 ECTSLecturer(s)
Course Type: core courseCourse Number: IS 801Credits: 8Course 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
Lecture Kick-off 09.09.19 Monday 17:15 – 20:30 IS/OPM 910: Area Seminar Information Systems & Operations Management ECTSLecturer(s)
Course Type: core courseCourse 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.
Machine Learning6 ECTSLecturer(s)
Course Type: elective courseCredits: 6Prerequisites
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
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 -
Management
MAN 802: Fundamentals of Non-Profit Management Science6 ECTSLecturer(s)
Course Type: core courseCourse Number: MAN 802Credits: 6Course 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
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 MAN 805: Applied Methods in Management Research6 ECTSLecturer(s)
Course Type: core courseCourse Number: MAN 805Credits: 6Course 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
Lecture 11.10.19 – 25.10.19 Friday 09:00 – 17:00 SO 422 MAN 806: Advances in Organization and Innovation Research6 ECTSLecturer(s)
Course Type: core courseCourse Number: MAN 806Credits: 6Course 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
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 MAN 910: Area Seminar ManagementECTSLecturer(s)
Course Type: core courseCourse Number: MAN 910Course 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.
MKT 903: Advanced Business Econometrics6 ECTSLecturer(s)
Course Type: core courseCourse Number: MKT 903Credits: 6Course 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
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 Machine Learning6 ECTSLecturer(s)
Course Type: elective courseCredits: 6Prerequisites
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
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 MAN 809: Theory Construction in the Social Sciences6 ECTSLecturer(s)
Course Type: elective courseCourse Number: MAN 809Credits: 6Course 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
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 -
Marketing
E 703: Advanced Econometrics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 703Credits: 8Course 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
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 MKT 801: Fundamentals of Marketing Research6 ECTSLecturer(s)
Course Type: core courseCourse Number: MKT 801Credits: 6Course 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
Lecture 06.09.19 – 06.12.19 Friday 10:15 – 13:30 L5, 2, room 107 MKT 903: Advanced Business Econometrics6 ECTSLecturer(s)
Course Type: core courseCourse Number: MKT 903Credits: 6Course 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
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 MKT 910: Area Seminar MarketingECTSLecturer(s)
Course Type: core courseCourse Number: MKT 910Course 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.
Machine Learning6 ECTSLecturer(s)
Course Type: elective courseCredits: 6Prerequisites
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
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 MKT 912: Performing Research and Publishing in Marketing8 ECTSLecturer(s)
Arch G. Woodside
Course Type: elective courseCourse Number: MKT 912Credits: 8Course 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 Schedule
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 -
Operations Management
E 701: Advanced Microeconomics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 701Credits: 8Prerequisites
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:
- Theory of consumer choice under certainty and uncertainty
- Theory of the firm, production cost and supply
- Equilibrium and welfare
- Strategic behavior under complete and incomplete information
- 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
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 E 703: Advanced Econometrics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 703Credits: 8Course 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
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 E700: Mathematics for Ecomomists6 ECTSLecturer(s)
Course Type: core courseCourse Number: E700Credits: 6Prerequisites
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
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 IS/OPM 910: Area Seminar Information Systems & Operations Management ECTSLecturer(s)
Course Type: core courseCourse 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.
OPM 805: Research Seminar Business Analytics8 ECTSLecturer(s)
Course Type: core courseCourse Number: OPM 805Credits: 8Course 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
Lecture Kick-Off 09.09.19 Monday 10:15 – 11:45 SO 322 OPM 901: Research Seminar Operations Management & Operations Research8 ECTSLecturer(s)
Course Type: core courseCourse Number: OPM 901Credits: 8Course 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
Lecture 05.09.19 – 05.12.19 Thursday 12:00 – 13:30 SO 318 Machine Learning6 ECTSLecturer(s)
Course Type: elective courseCredits: 6Prerequisites
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
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 OPM 803: Selected Topics in Nonlinear Optimization8 ECTSLecturer(s)
Course Type: elective courseCourse Number: OPM 803Credits: 8Prerequisites
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
Lecture 04.10.19 – 06.12.19 Friday 10:15 – 13:30 SO 322 -
Taxation
European Tax Law8 ECTSLecturer(s)
Course Type: core courseCredits: 8Prerequisites
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
Lecture 05.09.19 – 05.12.19 Thursday 12:00 – 13:30 W 114 ACC/TAX 910: Area Seminar Accounting and Taxation ECTSLecturer(s)
Course Type: core courseCourse 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.
ACC/TAX 920: Brown Bag Seminar ECTSLecturer(s)
Course Type: core courseCourse 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.
E 701: Advanced Microeconomics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 701Credits: 8Prerequisites
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:
- Theory of consumer choice under certainty and uncertainty
- Theory of the firm, production cost and supply
- Equilibrium and welfare
- Strategic behavior under complete and incomplete information
- 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
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 E 703: Advanced Econometrics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E 703Credits: 8Course 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
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 E700: Mathematics for Ecomomists6 ECTSLecturer(s)
Course Type: core courseCourse Number: E700Credits: 6Prerequisites
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
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 E702: Advanced Macroeconomics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: E702Credits: 8Prerequisites
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
Schedule
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 30.01.20 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 Machine Learning6 ECTSLecturer(s)
Course Type: elective courseCredits: 6Prerequisites
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
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 Statistics Refresher0 ECTSLecturer(s)
Course Type: elective courseCourse 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
Lecture 13.09.19 – 04.10.19 Friday 09:00 – 18:00 O 226/ 28 TAX 916: Applied Econometrics I8 ECTSLecturer(s)
Course Type: elective courseCourse Number: TAX 916Credits: 8Course 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
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