Credit: Anna Logue
Fall 2023
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Accounting
ACC / TAX 910: Area Seminar Accounting and TaxationECTSLecturer(s)
Course Type: core courseCourse Number: ACC / TAX 910Course 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.
Schedule
Seminar Seminar 04.09.23 – 04.12.23 Monday 15:30 – 17:00 O 048 Seminar 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 SO 318 ACC / TAX 916: Applied Econometrics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: ACC / 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 Lecture 04.09.23 – 04.12.23 Monday 08:30 – 10:00 O226–28 Lecture 06.09.23 – 06.12.23 Wednesday 08:30 – 10:00 SO318 ACC / TAX 920: Brown Bag SeminarECTSLecturer(s)
Course Type: core courseCourse Number: ACC / TAX 920Course 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.
Coursedates will be announced via email to registered participants.
Competences acquired
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.
Schedule
Seminar Seminar 06.09.23 – 13.09.23 Wednesday 13:45 – 15:15 O 226–28 Seminar 20.09.23 – 06.12.23 Wednesday 13:45 – 17:00 O 048 E 701: Advanced Microeconomics I (for Business)8 ECTSLecturer(s)
Yasmin Hoffmann
Course Type: core courseCourse Number: E 701Credits: 8Prerequisites
Mathematics for Economists, intermediate knowledge of microeconomics
Course Content
Please note that course times are not final at this point! More exercises will be added soon!
The course is aimed at doctoral students in all fields of business administration. It provides an introduction to microeconomic theory on a graduate level, highlighting aspects which are of specific relevance for business research.
The main topics covered include:
- Theory of consumer choice under certainty and uncertainty
- Theory of the firm, production cost and supply
- Markets, equilibrium, 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.
Form of assessment: Midterm and final exams: 80–100%, Exercises: Up to 20%
The course is also part of the TRR 266 Accounting for Transparency
Competences acquired
Understanding and critically evaluating the fundamental concepts of microeconomic theory, game theory and mechanism design; learning the relevant tools and underlying assumptions for economic analysis in ongoing research.
Schedule
Lecture Lecture 05.10.23 – 05.10.23 Thursday 13:45 – 15:15 O 226–28 Lecture 10.10.23 – 05.12.23 Tuesday 15:30 – 17:00 O 129 Lecture 12.10.23 – 07.12.23 Thursday 13:45 – 15:15 O 145 Lecture 12.10.23 – 30.11.23 Thursday 15:30 – 17:00 EO 145 Lecture 13.10.23 – 13.10.23 Friday 15:30 – 17:00 EO 242 Exam 08.11.23 – 08.11.23 Wednesday 10:15 – 11:45 B 243 Lecture 20.11.23 – 20.11.23 Monday 13:45 – 15:15 O 135 Tutorial Exercise 18.10.23 – 06.12.23 Wednesday 10:15 – 11:45 B6, 30–32 (E-F), room 211 Exercise 02.11.23 – 02.11.23 Thursday 13:45 – 15:15 O 145 Exercise 07.11.23 – 07.11.23 Tuesday 15:30 – 17:00 O 129 E 703: Advanced Econometrics I (for Business)8 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: Matrix Algebra and Probability Theory, Ordinary Least Squares, Maximum Likelihood, Generalized Method of Moments, 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
Teaching Assistant: Richard Winter Laura Arnemann
Grading: Written Exam (120 min) 90 %, problem sets 10 %
Please note: The course starts in October. The additional September sessions are voluntary refreshers for those students who do not have to take E700 Mathematics for Economists
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 10:15 – 11:45 SO 318 Lecture 07.09.23 – 07.12.23 Thursday 10:15 – 11:45 O048 Tutorial Exercise 06.09.23 – 06.12.23 Wednesday 15:30 – 17:00 257 (L7, 3–5) Exercise 08.09.23 – 08.12.23 Friday 10:15 – 11:45 O 226–28 E700: Mathematics for Economists (1st year)6 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)
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
So Jin Lee and Chang Liu
Schedule
Lecture Lecture 04.09.23 – 25.09.23 Monday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 05.09.23 – 26.09.23 Tuesday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 06.09.23 – 27.09.23 Wednesday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 07.09.23 – 28.09.23 Thursday 10:15 – 11:45 L7, 3–5 Room 001 Exam 06.10.23 – 06.10.23 Friday 08:00 – 10:00 Palace, Room SN 163 Retake exam 04.12.23 – 04.12.23 Monday 10:00 – 12:00 B 6, 30–32, E-F, Room 212 Tutorial Group 1 04.09.23 – 25.09.23 Monday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 04.09.23 – 25.09.23 Monday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 04.09.23 – 25.09.23 Monday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 04.09.23 – 25.09.23 Monday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 Group 1 05.09.23 – 26.09.23 Tuesday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 05.09.23 – 26.09.23 Tuesday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 211 Group 3 05.09.23 – 26.09.23 Tuesday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 05.09.23 – 26.09.23 Tuesday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 211 Group 1 06.09.23 – 27.09.23 Wednesday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 06.09.23 – 27.09.23 Wednesday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 06.09.23 – 27.09.23 Wednesday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 06.09.23 – 27.09.23 Wednesday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 Group 1 07.09.23 – 28.09.23 Thursday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 07.09.23 – 28.09.23 Thursday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 07.09.23 – 28.09.23 Thursday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 07.09.23 – 28.09.23 Thursday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 ACC 923: Corporate Sustainability and Decarbonization3 ECTSLecturer(s)
Course Type: elective courseCourse Number: ACC 923Credits: 3Course Content
This seminar is aimed at doctoral students at GESS. The seminar hosts speakers from academia and industry to discuss latest advances and challenges that companies face in the transition towards more sustainable business practices and net carbon emissions of zero. Topics covered include the economics and management of sustainability activities and emission abatement strategies across all sectors of the economy.
Course participants need to attend the seminar talks and the internal sessions. In the internal sessions, students are asked to present a paper and/
or take the role of a discussant. Readings may additionally include recent theory or empirical papers. Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on the covered topics and to identify promising avenues for future research. Moreover, students receive a training on how to present and evaluate papers in seminars and conferences.
Form of assessment: Participation (20%), Paper presentations and discussions (80%)
Schedule
Lecture Lecture 04.09.23 – 04.12.23 Monday 17:15 – 18:45 O 129 IS 808: Advanced Data Science Lab I (Network Science)6 ECTSLecturer(s)
Course Type: elective courseCourse Number: IS 808Credits: 6Course Content
The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing relational data, for example complex phenomena and systems, using techniques and methods from the domain of network science. The students will present their results and write a paper about their research.
Assessment: Term Paper 90% and Presentation 10%
Competences acquired
Students will be equipped with practical experience with conducting scientific data-science projects. They will train their presentation skills, learn to communicate in research projects and receive feedback.
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 17:00 L15, 1–6, 314–315 MET: Applied Data Science: Machine Learning for Economics and Business Data5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.
Recommended: Knowledge of basic statistics and prior experience with Python, R or Stata is helpful, but not necessary.
Course Content
This course provides a gentle introductory and practical approach to understanding and analyzing data using machine learning and artificial intelligence techniques with applications for economics and business data. Why these methods are an asset in the toolkit for students is clear: Machine learning methods may outperform conventional econometric methods in applications where data needs to be collected and classified or outcomes to be predicted. But the ultimate goal of economic analysis is often causal inference. We evaluate in which cases the promises of machine learning methods may enhance causal inference and at what cost incurred by imposing additional structure. We systematically review in the first part of the course supervised machine learning methods and artificial intelligence methods and benchmark them to conventional econometrics. In the second part, we discuss how to apply the techniques for causal inference and solve practical problems. The applications give plenty of opportunities to learn how to use the programming language Python. We will use the German Business Panel linked to large-scale datasets as a running example and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes.
In each session, one of the participants will present a method based on research papers and/
or program code, which we will discuss in light of real-world programming applications. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic. In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to apply the introduced methods or assess the results of the applications.
Learning outcomes: The specific applications cover a broad set of skills with a focus on application of machine learning and artificial intelligence techniques, analysis of big and unstructured data, classification, inference, writing of own program codes and reports, and opportunities for own research.
- Analytical Skills/
Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools. - Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
- Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/
or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses. - Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
- Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
- Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
- Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.
Form of assessment: Paper (technical report) (50 %), Presentation (25 %), Class Participation (25 %)
The course is also part of the TRR 266 Accounting for Transparency
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 15:30 – 17:00 O 226–28 MET: Intensive Longitudinal Methods in Contexts of Work and Learning5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
Basic knowledge of statistics; basic experience with the statistic software R.
Prior course registration is required and regular attendance is expected.
Preferably, the participants are planning a study with intensive longitudinal methods in the context of work and learning or have already conducted such a study.
Course Content
Content:
- research designs and research questions for intensive longitudinal methods (ILM)
- methods of data collection (diary methods, experience sampling, ecological momentary assessment, etc.)
- methods of data analysis (esp. multilevel analysis), and limitations
Competences acquired
Students are able to develop and evaluate research designs including ILM.
Students are able to develop and evaluate methods of data collection.
Students are able to distinguish and apply an adequate approaches to data analysis.
Students are able to interpret results and to discuss limitations.Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 L4, 1, room 004 RES (Bridge course): Lecture series “Data Science in Action”5 ECTSCourse Type: elective courseCourse Number: RES (Bridge course)Credits: 5Course Content
Our colleagues from the Mannheim Center for Data Science are offering a lecture series “Data Science in Action” for the upcoming fall term. The lecture series is online and starts on Thursday, 14 September 2023 at midday.
GESS doctoral students can attend the event as a bridge course. In order to receive the 5 ECTS points, you need to take part in at 80% of the lectures and write a 15 page essay (pass/fail assessment).
For more information and registration, please visit the website:
https://www.uni-mannheim.de/en/datascience/details/vortragsreihe-data-science-in-action/
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Finance
E 701: Advanced Microeconomics I (for Business)8 ECTSLecturer(s)
Yasmin Hoffmann
Course Type: core courseCourse Number: E 701Credits: 8Prerequisites
Mathematics for Economists, intermediate knowledge of microeconomics
Course Content
Please note that course times are not final at this point! More exercises will be added soon!
The course is aimed at doctoral students in all fields of business administration. It provides an introduction to microeconomic theory on a graduate level, highlighting aspects which are of specific relevance for business research.
The main topics covered include:
- Theory of consumer choice under certainty and uncertainty
- Theory of the firm, production cost and supply
- Markets, equilibrium, 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.
Form of assessment: Midterm and final exams: 80–100%, Exercises: Up to 20%
The course is also part of the TRR 266 Accounting for Transparency
Competences acquired
Understanding and critically evaluating the fundamental concepts of microeconomic theory, game theory and mechanism design; learning the relevant tools and underlying assumptions for economic analysis in ongoing research.
Schedule
Lecture Lecture 05.10.23 – 05.10.23 Thursday 13:45 – 15:15 O 226–28 Lecture 10.10.23 – 05.12.23 Tuesday 15:30 – 17:00 O 129 Lecture 12.10.23 – 07.12.23 Thursday 13:45 – 15:15 O 145 Lecture 12.10.23 – 30.11.23 Thursday 15:30 – 17:00 EO 145 Lecture 13.10.23 – 13.10.23 Friday 15:30 – 17:00 EO 242 Exam 08.11.23 – 08.11.23 Wednesday 10:15 – 11:45 B 243 Lecture 20.11.23 – 20.11.23 Monday 13:45 – 15:15 O 135 Tutorial Exercise 18.10.23 – 06.12.23 Wednesday 10:15 – 11:45 B6, 30–32 (E-F), room 211 Exercise 02.11.23 – 02.11.23 Thursday 13:45 – 15:15 O 145 Exercise 07.11.23 – 07.11.23 Tuesday 15:30 – 17:00 O 129 E 703: Advanced Econometrics I (for Business)8 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: Matrix Algebra and Probability Theory, Ordinary Least Squares, Maximum Likelihood, Generalized Method of Moments, 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
Teaching Assistant: Richard Winter Laura Arnemann
Grading: Written Exam (120 min) 90 %, problem sets 10 %
Please note: The course starts in October. The additional September sessions are voluntary refreshers for those students who do not have to take E700 Mathematics for Economists
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 10:15 – 11:45 SO 318 Lecture 07.09.23 – 07.12.23 Thursday 10:15 – 11:45 O048 Tutorial Exercise 06.09.23 – 06.12.23 Wednesday 15:30 – 17:00 257 (L7, 3–5) Exercise 08.09.23 – 08.12.23 Friday 10:15 – 11:45 O 226–28 E700: Mathematics for Economists (1st year)6 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)
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
So Jin Lee and Chang Liu
Schedule
Lecture Lecture 04.09.23 – 25.09.23 Monday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 05.09.23 – 26.09.23 Tuesday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 06.09.23 – 27.09.23 Wednesday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 07.09.23 – 28.09.23 Thursday 10:15 – 11:45 L7, 3–5 Room 001 Exam 06.10.23 – 06.10.23 Friday 08:00 – 10:00 Palace, Room SN 163 Retake exam 04.12.23 – 04.12.23 Monday 10:00 – 12:00 B 6, 30–32, E-F, Room 212 Tutorial Group 1 04.09.23 – 25.09.23 Monday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 04.09.23 – 25.09.23 Monday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 04.09.23 – 25.09.23 Monday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 04.09.23 – 25.09.23 Monday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 Group 1 05.09.23 – 26.09.23 Tuesday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 05.09.23 – 26.09.23 Tuesday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 211 Group 3 05.09.23 – 26.09.23 Tuesday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 05.09.23 – 26.09.23 Tuesday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 211 Group 1 06.09.23 – 27.09.23 Wednesday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 06.09.23 – 27.09.23 Wednesday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 06.09.23 – 27.09.23 Wednesday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 06.09.23 – 27.09.23 Wednesday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 Group 1 07.09.23 – 28.09.23 Thursday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 07.09.23 – 28.09.23 Thursday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 07.09.23 – 28.09.23 Thursday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 07.09.23 – 28.09.23 Thursday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 FIN 801: Asset Pricing8 ECTSLecturer(s)
Course Type: core courseCourse Number: FIN 801Credits: 8Prerequisites
Formal: E 700
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.
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.
Form of assessment: Written Exam (90 minutes) 60%, Class Participation (incl. term paper)
Competences acquired
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.
Schedule
Lecture Lecture 03.11.23 – 24.11.23 Friday 12:00 – 17:00 L 9, 1–2, Room 409 Exam 01.12.23 – 01.12.23 Friday 12:00 – 13:00 L 9, 1–2, Room 409 Student Presentations 08.12.23 – 08.12.23 Friday 12:00 – 17:00 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.
Form of assessment: Oral participation.
Seminar Dates are announced here.
Competences acquired
Students will learn to follow-up with and discuss about current research topics in finance. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.
Schedule
Seminar Seminar 04.09.23 – 04.12.23 Monday (irregularly, check schedule website for exact dates) 15:30 – 17:00 L9, 1–2, room 004 ACC 923: Corporate Sustainability and Decarbonization3 ECTSLecturer(s)
Course Type: elective courseCourse Number: ACC 923Credits: 3Course Content
This seminar is aimed at doctoral students at GESS. The seminar hosts speakers from academia and industry to discuss latest advances and challenges that companies face in the transition towards more sustainable business practices and net carbon emissions of zero. Topics covered include the economics and management of sustainability activities and emission abatement strategies across all sectors of the economy.
Course participants need to attend the seminar talks and the internal sessions. In the internal sessions, students are asked to present a paper and/
or take the role of a discussant. Readings may additionally include recent theory or empirical papers. Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on the covered topics and to identify promising avenues for future research. Moreover, students receive a training on how to present and evaluate papers in seminars and conferences.
Form of assessment: Participation (20%), Paper presentations and discussions (80%)
Schedule
Lecture Lecture 04.09.23 – 04.12.23 Monday 17:15 – 18:45 O 129 IS 808: Advanced Data Science Lab I (Network Science)6 ECTSLecturer(s)
Course Type: elective courseCourse Number: IS 808Credits: 6Course Content
The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing relational data, for example complex phenomena and systems, using techniques and methods from the domain of network science. The students will present their results and write a paper about their research.
Assessment: Term Paper 90% and Presentation 10%
Competences acquired
Students will be equipped with practical experience with conducting scientific data-science projects. They will train their presentation skills, learn to communicate in research projects and receive feedback.
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 17:00 L15, 1–6, 314–315 MET: Applied Data Science: Machine Learning for Economics and Business Data5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.
Recommended: Knowledge of basic statistics and prior experience with Python, R or Stata is helpful, but not necessary.
Course Content
This course provides a gentle introductory and practical approach to understanding and analyzing data using machine learning and artificial intelligence techniques with applications for economics and business data. Why these methods are an asset in the toolkit for students is clear: Machine learning methods may outperform conventional econometric methods in applications where data needs to be collected and classified or outcomes to be predicted. But the ultimate goal of economic analysis is often causal inference. We evaluate in which cases the promises of machine learning methods may enhance causal inference and at what cost incurred by imposing additional structure. We systematically review in the first part of the course supervised machine learning methods and artificial intelligence methods and benchmark them to conventional econometrics. In the second part, we discuss how to apply the techniques for causal inference and solve practical problems. The applications give plenty of opportunities to learn how to use the programming language Python. We will use the German Business Panel linked to large-scale datasets as a running example and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes.
In each session, one of the participants will present a method based on research papers and/
or program code, which we will discuss in light of real-world programming applications. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic. In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to apply the introduced methods or assess the results of the applications.
Learning outcomes: The specific applications cover a broad set of skills with a focus on application of machine learning and artificial intelligence techniques, analysis of big and unstructured data, classification, inference, writing of own program codes and reports, and opportunities for own research.
- Analytical Skills/
Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools. - Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
- Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/
or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses. - Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
- Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
- Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
- Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.
Form of assessment: Paper (technical report) (50 %), Presentation (25 %), Class Participation (25 %)
The course is also part of the TRR 266 Accounting for Transparency
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 15:30 – 17:00 O 226–28 MET: Intensive Longitudinal Methods in Contexts of Work and Learning5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
Basic knowledge of statistics; basic experience with the statistic software R.
Prior course registration is required and regular attendance is expected.
Preferably, the participants are planning a study with intensive longitudinal methods in the context of work and learning or have already conducted such a study.
Course Content
Content:
- research designs and research questions for intensive longitudinal methods (ILM)
- methods of data collection (diary methods, experience sampling, ecological momentary assessment, etc.)
- methods of data analysis (esp. multilevel analysis), and limitations
Competences acquired
Students are able to develop and evaluate research designs including ILM.
Students are able to develop and evaluate methods of data collection.
Students are able to distinguish and apply an adequate approaches to data analysis.
Students are able to interpret results and to discuss limitations.Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 L4, 1, room 004 RES (Bridge course): Lecture series “Data Science in Action”5 ECTSCourse Type: elective courseCourse Number: RES (Bridge course)Credits: 5Course Content
Our colleagues from the Mannheim Center for Data Science are offering a lecture series “Data Science in Action” for the upcoming fall term. The lecture series is online and starts on Thursday, 14 September 2023 at midday.
GESS doctoral students can attend the event as a bridge course. In order to receive the 5 ECTS points, you need to take part in at 80% of the lectures and write a 15 page essay (pass/fail assessment).
For more information and registration, please visit the website:
https://www.uni-mannheim.de/en/datascience/details/vortragsreihe-data-science-in-action/
-
Information Systems
IS / OPM 910: Area Seminar Information Systems and Operations ManagementECTSLecturer(s)
Course Type: core courseCourse Number: IS / OPM 910Course 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.
Competences acquired
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.
Schedule
Seminar Seminar 06.09.23 – 06.12.23 Wednesday 12:00 – 13:30 O 142 Seminar 26.10.23 – 26.10.23 Thursday 12:00 – 13:30 O 148 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.
Form of assessment: Assignment, Presentation, Discussion
Kick-off date: 18/
09/2023, 3.30 – 5 pm Competences acquired
PhD students are introduced to the exciting field of design science research. They understand the basic principles for successfully carrying out design science research.
Schedule
Lecture Kick-off 18.09.23 – 18.09.23 Monday 15:30 – 17:00 tba IS 901: Epistemological Foundations8 ECTSLecturer(s)
Course Type: core courseCourse Number: IS 901Credits: 8Course Content
This course is designed for doctoral students in information systems and other managerial disciplines. It provides a basic understanding of philosophy of science and its epistemological foundations. On the one hand, the course will focus on those concepts which derive knowledge from observation, induction, and refutation of facts. Furthermore, it also takes experiments as well as the new experimentalism into account in order to refer to those disciplines that focus on the evaluation of artifacts like prototypes and algorithms for example. Thus, the underlying epistemological foundations are of central interest to every doctoral student who studies the structure and behavior of information systems and operations/
logistics phenomena. The course will be offered in an interactive style. All doctoral students have to offer at least one presentation and a documentation regarding a specific epistemological stance. Assignment of topics will be conducted by the lecturer.
Please note: Course dates will be arranged in consultation with the participants.
ACC 923: Corporate Sustainability and Decarbonization3 ECTSLecturer(s)
Course Type: elective courseCourse Number: ACC 923Credits: 3Course Content
This seminar is aimed at doctoral students at GESS. The seminar hosts speakers from academia and industry to discuss latest advances and challenges that companies face in the transition towards more sustainable business practices and net carbon emissions of zero. Topics covered include the economics and management of sustainability activities and emission abatement strategies across all sectors of the economy.
Course participants need to attend the seminar talks and the internal sessions. In the internal sessions, students are asked to present a paper and/
or take the role of a discussant. Readings may additionally include recent theory or empirical papers. Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on the covered topics and to identify promising avenues for future research. Moreover, students receive a training on how to present and evaluate papers in seminars and conferences.
Form of assessment: Participation (20%), Paper presentations and discussions (80%)
Schedule
Lecture Lecture 04.09.23 – 04.12.23 Monday 17:15 – 18:45 O 129 IS 808: Advanced Data Science Lab I (Network Science)6 ECTSLecturer(s)
Course Type: elective courseCourse Number: IS 808Credits: 6Course Content
The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing relational data, for example complex phenomena and systems, using techniques and methods from the domain of network science. The students will present their results and write a paper about their research.
Assessment: Term Paper 90% and Presentation 10%
Competences acquired
Students will be equipped with practical experience with conducting scientific data-science projects. They will train their presentation skills, learn to communicate in research projects and receive feedback.
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 17:00 L15, 1–6, 314–315 MET: Applied Data Science: Machine Learning for Economics and Business Data5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.
Recommended: Knowledge of basic statistics and prior experience with Python, R or Stata is helpful, but not necessary.
Course Content
This course provides a gentle introductory and practical approach to understanding and analyzing data using machine learning and artificial intelligence techniques with applications for economics and business data. Why these methods are an asset in the toolkit for students is clear: Machine learning methods may outperform conventional econometric methods in applications where data needs to be collected and classified or outcomes to be predicted. But the ultimate goal of economic analysis is often causal inference. We evaluate in which cases the promises of machine learning methods may enhance causal inference and at what cost incurred by imposing additional structure. We systematically review in the first part of the course supervised machine learning methods and artificial intelligence methods and benchmark them to conventional econometrics. In the second part, we discuss how to apply the techniques for causal inference and solve practical problems. The applications give plenty of opportunities to learn how to use the programming language Python. We will use the German Business Panel linked to large-scale datasets as a running example and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes.
In each session, one of the participants will present a method based on research papers and/
or program code, which we will discuss in light of real-world programming applications. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic. In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to apply the introduced methods or assess the results of the applications.
Learning outcomes: The specific applications cover a broad set of skills with a focus on application of machine learning and artificial intelligence techniques, analysis of big and unstructured data, classification, inference, writing of own program codes and reports, and opportunities for own research.
- Analytical Skills/
Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools. - Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
- Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/
or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses. - Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
- Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
- Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
- Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.
Form of assessment: Paper (technical report) (50 %), Presentation (25 %), Class Participation (25 %)
The course is also part of the TRR 266 Accounting for Transparency
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 15:30 – 17:00 O 226–28 MET: Intensive Longitudinal Methods in Contexts of Work and Learning5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
Basic knowledge of statistics; basic experience with the statistic software R.
Prior course registration is required and regular attendance is expected.
Preferably, the participants are planning a study with intensive longitudinal methods in the context of work and learning or have already conducted such a study.
Course Content
Content:
- research designs and research questions for intensive longitudinal methods (ILM)
- methods of data collection (diary methods, experience sampling, ecological momentary assessment, etc.)
- methods of data analysis (esp. multilevel analysis), and limitations
Competences acquired
Students are able to develop and evaluate research designs including ILM.
Students are able to develop and evaluate methods of data collection.
Students are able to distinguish and apply an adequate approaches to data analysis.
Students are able to interpret results and to discuss limitations.Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 L4, 1, room 004 RES (Bridge course): Lecture series “Data Science in Action”5 ECTSCourse Type: elective courseCourse Number: RES (Bridge course)Credits: 5Course Content
Our colleagues from the Mannheim Center for Data Science are offering a lecture series “Data Science in Action” for the upcoming fall term. The lecture series is online and starts on Thursday, 14 September 2023 at midday.
GESS doctoral students can attend the event as a bridge course. In order to receive the 5 ECTS points, you need to take part in at 80% of the lectures and write a 15 page essay (pass/fail assessment).
For more information and registration, please visit the website:
https://www.uni-mannheim.de/en/datascience/details/vortragsreihe-data-science-in-action/
- Analytical Skills/
-
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”.
Competences acquired
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 Lecture 18.09.23 – 18.09.23 Monday 14:30 – 16:30 EO 256 Lecture 09.10.23 – 09.10.23 Monday 14:30 – 15:30 EO 256 Lecture 20.11.23 – 20.11.23 Monday 10:00 – 17:00 EO 256 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
Form of assessment: Oral exam (20 minutes) 75 %, presentation 25 %
Competences acquired
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
Schedule
Seminar Seminar 03.11.23 – 17.11.23 Friday 09:00 – 17:00 EO 256 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.
Form of Assessment: Presentation 50%, Discussion 50%
Competences acquired
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.
Schedule
Lecture Lecture 19.09.23 – 19.09.23 Tuesday 14:00 – 17:00 EO 256 Lecture 24.10.23 – 24.10.23 Tuesday 14:00 – 18:00 EO 256 Lecture 25.10.23 – 25.10.23 Wednesday 14:00 – 18:00 EO 256 Lecture 31.10.23 – 31.10.23 Tuesday 14:00 – 18:00 EO 256 Lecture 07.11.23 – 07.11.23 Tuesday 14:00 – 18:00 EO 256 MAN 809: Theory Construction in the Social Sciences6 ECTSLecturer(s)
Course Type: core 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.
Form of assessment: Assignment 40 %, Paper 50 %, Class Participation 10 %
Competences acquired
In essence, the course provides an opportunity to compose the front section of an academic manuscript and receive constructive feedback.
Schedule
Lecture Lecture 11.10.23 – 11.10.23 Wednesday 10:00 – 13:00 509 (L9, 7) Lecture 09.11.23 – 09.11.23 Thursday 10:00 – 17:00 EO 256 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.
Competences acquired
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.
Schedule
Seminar Seminar 20.09.23 – 13.12.23 Wednesday 13:45 – 15:15 SO 318 ACC 923: Corporate Sustainability and Decarbonization3 ECTSLecturer(s)
Course Type: elective courseCourse Number: ACC 923Credits: 3Course Content
This seminar is aimed at doctoral students at GESS. The seminar hosts speakers from academia and industry to discuss latest advances and challenges that companies face in the transition towards more sustainable business practices and net carbon emissions of zero. Topics covered include the economics and management of sustainability activities and emission abatement strategies across all sectors of the economy.
Course participants need to attend the seminar talks and the internal sessions. In the internal sessions, students are asked to present a paper and/
or take the role of a discussant. Readings may additionally include recent theory or empirical papers. Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on the covered topics and to identify promising avenues for future research. Moreover, students receive a training on how to present and evaluate papers in seminars and conferences.
Form of assessment: Participation (20%), Paper presentations and discussions (80%)
Schedule
Lecture Lecture 04.09.23 – 04.12.23 Monday 17:15 – 18:45 O 129 IS 808: Advanced Data Science Lab I (Network Science)6 ECTSLecturer(s)
Course Type: elective courseCourse Number: IS 808Credits: 6Course Content
The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing relational data, for example complex phenomena and systems, using techniques and methods from the domain of network science. The students will present their results and write a paper about their research.
Assessment: Term Paper 90% and Presentation 10%
Competences acquired
Students will be equipped with practical experience with conducting scientific data-science projects. They will train their presentation skills, learn to communicate in research projects and receive feedback.
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 17:00 L15, 1–6, 314–315 MET: Applied Data Science: Machine Learning for Economics and Business Data5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.
Recommended: Knowledge of basic statistics and prior experience with Python, R or Stata is helpful, but not necessary.
Course Content
This course provides a gentle introductory and practical approach to understanding and analyzing data using machine learning and artificial intelligence techniques with applications for economics and business data. Why these methods are an asset in the toolkit for students is clear: Machine learning methods may outperform conventional econometric methods in applications where data needs to be collected and classified or outcomes to be predicted. But the ultimate goal of economic analysis is often causal inference. We evaluate in which cases the promises of machine learning methods may enhance causal inference and at what cost incurred by imposing additional structure. We systematically review in the first part of the course supervised machine learning methods and artificial intelligence methods and benchmark them to conventional econometrics. In the second part, we discuss how to apply the techniques for causal inference and solve practical problems. The applications give plenty of opportunities to learn how to use the programming language Python. We will use the German Business Panel linked to large-scale datasets as a running example and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes.
In each session, one of the participants will present a method based on research papers and/
or program code, which we will discuss in light of real-world programming applications. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic. In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to apply the introduced methods or assess the results of the applications.
Learning outcomes: The specific applications cover a broad set of skills with a focus on application of machine learning and artificial intelligence techniques, analysis of big and unstructured data, classification, inference, writing of own program codes and reports, and opportunities for own research.
- Analytical Skills/
Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools. - Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
- Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/
or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses. - Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
- Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
- Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
- Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.
Form of assessment: Paper (technical report) (50 %), Presentation (25 %), Class Participation (25 %)
The course is also part of the TRR 266 Accounting for Transparency
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 15:30 – 17:00 O 226–28 MET: Intensive Longitudinal Methods in Contexts of Work and Learning5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
Basic knowledge of statistics; basic experience with the statistic software R.
Prior course registration is required and regular attendance is expected.
Preferably, the participants are planning a study with intensive longitudinal methods in the context of work and learning or have already conducted such a study.
Course Content
Content:
- research designs and research questions for intensive longitudinal methods (ILM)
- methods of data collection (diary methods, experience sampling, ecological momentary assessment, etc.)
- methods of data analysis (esp. multilevel analysis), and limitations
Competences acquired
Students are able to develop and evaluate research designs including ILM.
Students are able to develop and evaluate methods of data collection.
Students are able to distinguish and apply an adequate approaches to data analysis.
Students are able to interpret results and to discuss limitations.Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 L4, 1, room 004 RES (Bridge course): Lecture series “Data Science in Action”5 ECTSCourse Type: elective courseCourse Number: RES (Bridge course)Credits: 5Course Content
Our colleagues from the Mannheim Center for Data Science are offering a lecture series “Data Science in Action” for the upcoming fall term. The lecture series is online and starts on Thursday, 14 September 2023 at midday.
GESS doctoral students can attend the event as a bridge course. In order to receive the 5 ECTS points, you need to take part in at 80% of the lectures and write a 15 page essay (pass/fail assessment).
For more information and registration, please visit the website:
https://www.uni-mannheim.de/en/datascience/details/vortragsreihe-data-science-in-action/
-
Marketing
E 703: Advanced Econometrics I (for Business)8 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: Matrix Algebra and Probability Theory, Ordinary Least Squares, Maximum Likelihood, Generalized Method of Moments, 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
Teaching Assistant: Richard Winter Laura Arnemann
Grading: Written Exam (120 min) 90 %, problem sets 10 %
Please note: The course starts in October. The additional September sessions are voluntary refreshers for those students who do not have to take E700 Mathematics for Economists
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 10:15 – 11:45 SO 318 Lecture 07.09.23 – 07.12.23 Thursday 10:15 – 11:45 O048 Tutorial Exercise 06.09.23 – 06.12.23 Wednesday 15:30 – 17:00 257 (L7, 3–5) Exercise 08.09.23 – 08.12.23 Friday 10:15 – 11:45 O 226–28 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.
Form of assessment: Essay: 30%, Presentation: 70%
Competences acquired
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.
Schedule
Lecture Lecture 07.09.23 – 07.12.23 Thursday 13:45 – 15:15 room 107 (L5, 2) 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 Lecture 29.09.23 – 29.09.23 Friday 09:30 – 17:00 L 5, 2, room 107 Lecture 13.10.23 – 13.10.23 Friday 09:30 – 17:00 L 5, 2, room 107 Lecture 27.10.23 – 27.10.23 Friday 09:30 – 17:00 L 5, 2, room 107 Lecture 10.11.23 – 10.11.23 Friday 09:30 – 17:00 L 5, 2, room 107 Lecture 24.11.23 – 24.11.23 Friday 09:30 – 17:00 L5, 2, room 107 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.
Seminar Dates are announced here.
Competences acquired
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.
Schedule
Seminar Seminar 28.09.23 – 07.12.23 Thursday (irregularly, check talk schedule) 11:00 – 12:30 tba ACC 923: Corporate Sustainability and Decarbonization3 ECTSLecturer(s)
Course Type: elective courseCourse Number: ACC 923Credits: 3Course Content
This seminar is aimed at doctoral students at GESS. The seminar hosts speakers from academia and industry to discuss latest advances and challenges that companies face in the transition towards more sustainable business practices and net carbon emissions of zero. Topics covered include the economics and management of sustainability activities and emission abatement strategies across all sectors of the economy.
Course participants need to attend the seminar talks and the internal sessions. In the internal sessions, students are asked to present a paper and/
or take the role of a discussant. Readings may additionally include recent theory or empirical papers. Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on the covered topics and to identify promising avenues for future research. Moreover, students receive a training on how to present and evaluate papers in seminars and conferences.
Form of assessment: Participation (20%), Paper presentations and discussions (80%)
Schedule
Lecture Lecture 04.09.23 – 04.12.23 Monday 17:15 – 18:45 O 129 IS 808: Advanced Data Science Lab I (Network Science)6 ECTSLecturer(s)
Course Type: elective courseCourse Number: IS 808Credits: 6Course Content
The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing relational data, for example complex phenomena and systems, using techniques and methods from the domain of network science. The students will present their results and write a paper about their research.
Assessment: Term Paper 90% and Presentation 10%
Competences acquired
Students will be equipped with practical experience with conducting scientific data-science projects. They will train their presentation skills, learn to communicate in research projects and receive feedback.
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 17:00 L15, 1–6, 314–315 MET: Applied Data Science: Machine Learning for Economics and Business Data5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.
Recommended: Knowledge of basic statistics and prior experience with Python, R or Stata is helpful, but not necessary.
Course Content
This course provides a gentle introductory and practical approach to understanding and analyzing data using machine learning and artificial intelligence techniques with applications for economics and business data. Why these methods are an asset in the toolkit for students is clear: Machine learning methods may outperform conventional econometric methods in applications where data needs to be collected and classified or outcomes to be predicted. But the ultimate goal of economic analysis is often causal inference. We evaluate in which cases the promises of machine learning methods may enhance causal inference and at what cost incurred by imposing additional structure. We systematically review in the first part of the course supervised machine learning methods and artificial intelligence methods and benchmark them to conventional econometrics. In the second part, we discuss how to apply the techniques for causal inference and solve practical problems. The applications give plenty of opportunities to learn how to use the programming language Python. We will use the German Business Panel linked to large-scale datasets as a running example and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes.
In each session, one of the participants will present a method based on research papers and/
or program code, which we will discuss in light of real-world programming applications. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic. In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to apply the introduced methods or assess the results of the applications.
Learning outcomes: The specific applications cover a broad set of skills with a focus on application of machine learning and artificial intelligence techniques, analysis of big and unstructured data, classification, inference, writing of own program codes and reports, and opportunities for own research.
- Analytical Skills/
Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools. - Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
- Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/
or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses. - Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
- Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
- Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
- Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.
Form of assessment: Paper (technical report) (50 %), Presentation (25 %), Class Participation (25 %)
The course is also part of the TRR 266 Accounting for Transparency
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 15:30 – 17:00 O 226–28 MET: Intensive Longitudinal Methods in Contexts of Work and Learning5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
Basic knowledge of statistics; basic experience with the statistic software R.
Prior course registration is required and regular attendance is expected.
Preferably, the participants are planning a study with intensive longitudinal methods in the context of work and learning or have already conducted such a study.
Course Content
Content:
- research designs and research questions for intensive longitudinal methods (ILM)
- methods of data collection (diary methods, experience sampling, ecological momentary assessment, etc.)
- methods of data analysis (esp. multilevel analysis), and limitations
Competences acquired
Students are able to develop and evaluate research designs including ILM.
Students are able to develop and evaluate methods of data collection.
Students are able to distinguish and apply an adequate approaches to data analysis.
Students are able to interpret results and to discuss limitations.Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 L4, 1, room 004 RES (Bridge course): Lecture series “Data Science in Action”5 ECTSCourse Type: elective courseCourse Number: RES (Bridge course)Credits: 5Course Content
Our colleagues from the Mannheim Center for Data Science are offering a lecture series “Data Science in Action” for the upcoming fall term. The lecture series is online and starts on Thursday, 14 September 2023 at midday.
GESS doctoral students can attend the event as a bridge course. In order to receive the 5 ECTS points, you need to take part in at 80% of the lectures and write a 15 page essay (pass/fail assessment).
For more information and registration, please visit the website:
https://www.uni-mannheim.de/en/datascience/details/vortragsreihe-data-science-in-action/
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Operations Management
E 701: Advanced Microeconomics I (for Business)8 ECTSLecturer(s)
Yasmin Hoffmann
Course Type: core courseCourse Number: E 701Credits: 8Prerequisites
Mathematics for Economists, intermediate knowledge of microeconomics
Course Content
Please note that course times are not final at this point! More exercises will be added soon!
The course is aimed at doctoral students in all fields of business administration. It provides an introduction to microeconomic theory on a graduate level, highlighting aspects which are of specific relevance for business research.
The main topics covered include:
- Theory of consumer choice under certainty and uncertainty
- Theory of the firm, production cost and supply
- Markets, equilibrium, 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.
Form of assessment: Midterm and final exams: 80–100%, Exercises: Up to 20%
The course is also part of the TRR 266 Accounting for Transparency
Competences acquired
Understanding and critically evaluating the fundamental concepts of microeconomic theory, game theory and mechanism design; learning the relevant tools and underlying assumptions for economic analysis in ongoing research.
Schedule
Lecture Lecture 05.10.23 – 05.10.23 Thursday 13:45 – 15:15 O 226–28 Lecture 10.10.23 – 05.12.23 Tuesday 15:30 – 17:00 O 129 Lecture 12.10.23 – 07.12.23 Thursday 13:45 – 15:15 O 145 Lecture 12.10.23 – 30.11.23 Thursday 15:30 – 17:00 EO 145 Lecture 13.10.23 – 13.10.23 Friday 15:30 – 17:00 EO 242 Exam 08.11.23 – 08.11.23 Wednesday 10:15 – 11:45 B 243 Lecture 20.11.23 – 20.11.23 Monday 13:45 – 15:15 O 135 Tutorial Exercise 18.10.23 – 06.12.23 Wednesday 10:15 – 11:45 B6, 30–32 (E-F), room 211 Exercise 02.11.23 – 02.11.23 Thursday 13:45 – 15:15 O 145 Exercise 07.11.23 – 07.11.23 Tuesday 15:30 – 17:00 O 129 E 703: Advanced Econometrics I (for Business)8 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: Matrix Algebra and Probability Theory, Ordinary Least Squares, Maximum Likelihood, Generalized Method of Moments, 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
Teaching Assistant: Richard Winter Laura Arnemann
Grading: Written Exam (120 min) 90 %, problem sets 10 %
Please note: The course starts in October. The additional September sessions are voluntary refreshers for those students who do not have to take E700 Mathematics for Economists
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 10:15 – 11:45 SO 318 Lecture 07.09.23 – 07.12.23 Thursday 10:15 – 11:45 O048 Tutorial Exercise 06.09.23 – 06.12.23 Wednesday 15:30 – 17:00 257 (L7, 3–5) Exercise 08.09.23 – 08.12.23 Friday 10:15 – 11:45 O 226–28 E700: Mathematics for Economists (1st year)6 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)
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
So Jin Lee and Chang Liu
Schedule
Lecture Lecture 04.09.23 – 25.09.23 Monday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 05.09.23 – 26.09.23 Tuesday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 06.09.23 – 27.09.23 Wednesday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 07.09.23 – 28.09.23 Thursday 10:15 – 11:45 L7, 3–5 Room 001 Exam 06.10.23 – 06.10.23 Friday 08:00 – 10:00 Palace, Room SN 163 Retake exam 04.12.23 – 04.12.23 Monday 10:00 – 12:00 B 6, 30–32, E-F, Room 212 Tutorial Group 1 04.09.23 – 25.09.23 Monday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 04.09.23 – 25.09.23 Monday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 04.09.23 – 25.09.23 Monday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 04.09.23 – 25.09.23 Monday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 Group 1 05.09.23 – 26.09.23 Tuesday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 05.09.23 – 26.09.23 Tuesday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 211 Group 3 05.09.23 – 26.09.23 Tuesday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 05.09.23 – 26.09.23 Tuesday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 211 Group 1 06.09.23 – 27.09.23 Wednesday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 06.09.23 – 27.09.23 Wednesday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 06.09.23 – 27.09.23 Wednesday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 06.09.23 – 27.09.23 Wednesday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 Group 1 07.09.23 – 28.09.23 Thursday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 07.09.23 – 28.09.23 Thursday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 07.09.23 – 28.09.23 Thursday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 07.09.23 – 28.09.23 Thursday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 IS / OPM 910: Area Seminar Information Systems and Operations ManagementECTSLecturer(s)
Course Type: core courseCourse Number: IS / OPM 910Course 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.
Competences acquired
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.
Schedule
Seminar Seminar 06.09.23 – 06.12.23 Wednesday 12:00 – 13:30 O 142 Seminar 26.10.23 – 26.10.23 Thursday 12:00 – 13:30 O 148 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.
Form of assessment: Paper 70 %, Presentation 30 %
Competences acquired
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.
Schedule
Lecture Lecture 04.09.23 – 25.09.23 Monday 15:30 – 17:00 SO 322 Lecture 02.10.23 – 04.12.23 Monday 10:15 – 11:45 tba 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.
Form of assessment: Presentation, Assignment
Competences acquired
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.
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 12:00 – 13:30 SO 318 ACC 923: Corporate Sustainability and Decarbonization3 ECTSLecturer(s)
Course Type: elective courseCourse Number: ACC 923Credits: 3Course Content
This seminar is aimed at doctoral students at GESS. The seminar hosts speakers from academia and industry to discuss latest advances and challenges that companies face in the transition towards more sustainable business practices and net carbon emissions of zero. Topics covered include the economics and management of sustainability activities and emission abatement strategies across all sectors of the economy.
Course participants need to attend the seminar talks and the internal sessions. In the internal sessions, students are asked to present a paper and/
or take the role of a discussant. Readings may additionally include recent theory or empirical papers. Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on the covered topics and to identify promising avenues for future research. Moreover, students receive a training on how to present and evaluate papers in seminars and conferences.
Form of assessment: Participation (20%), Paper presentations and discussions (80%)
Schedule
Lecture Lecture 04.09.23 – 04.12.23 Monday 17:15 – 18:45 O 129 IS 808: Advanced Data Science Lab I (Network Science)6 ECTSLecturer(s)
Course Type: elective courseCourse Number: IS 808Credits: 6Course Content
The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing relational data, for example complex phenomena and systems, using techniques and methods from the domain of network science. The students will present their results and write a paper about their research.
Assessment: Term Paper 90% and Presentation 10%
Competences acquired
Students will be equipped with practical experience with conducting scientific data-science projects. They will train their presentation skills, learn to communicate in research projects and receive feedback.
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 17:00 L15, 1–6, 314–315 MET: Applied Data Science: Machine Learning for Economics and Business Data5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.
Recommended: Knowledge of basic statistics and prior experience with Python, R or Stata is helpful, but not necessary.
Course Content
This course provides a gentle introductory and practical approach to understanding and analyzing data using machine learning and artificial intelligence techniques with applications for economics and business data. Why these methods are an asset in the toolkit for students is clear: Machine learning methods may outperform conventional econometric methods in applications where data needs to be collected and classified or outcomes to be predicted. But the ultimate goal of economic analysis is often causal inference. We evaluate in which cases the promises of machine learning methods may enhance causal inference and at what cost incurred by imposing additional structure. We systematically review in the first part of the course supervised machine learning methods and artificial intelligence methods and benchmark them to conventional econometrics. In the second part, we discuss how to apply the techniques for causal inference and solve practical problems. The applications give plenty of opportunities to learn how to use the programming language Python. We will use the German Business Panel linked to large-scale datasets as a running example and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes.
In each session, one of the participants will present a method based on research papers and/
or program code, which we will discuss in light of real-world programming applications. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic. In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to apply the introduced methods or assess the results of the applications.
Learning outcomes: The specific applications cover a broad set of skills with a focus on application of machine learning and artificial intelligence techniques, analysis of big and unstructured data, classification, inference, writing of own program codes and reports, and opportunities for own research.
- Analytical Skills/
Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools. - Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
- Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/
or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses. - Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
- Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
- Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
- Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.
Form of assessment: Paper (technical report) (50 %), Presentation (25 %), Class Participation (25 %)
The course is also part of the TRR 266 Accounting for Transparency
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 15:30 – 17:00 O 226–28 MET: Intensive Longitudinal Methods in Contexts of Work and Learning5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
Basic knowledge of statistics; basic experience with the statistic software R.
Prior course registration is required and regular attendance is expected.
Preferably, the participants are planning a study with intensive longitudinal methods in the context of work and learning or have already conducted such a study.
Course Content
Content:
- research designs and research questions for intensive longitudinal methods (ILM)
- methods of data collection (diary methods, experience sampling, ecological momentary assessment, etc.)
- methods of data analysis (esp. multilevel analysis), and limitations
Competences acquired
Students are able to develop and evaluate research designs including ILM.
Students are able to develop and evaluate methods of data collection.
Students are able to distinguish and apply an adequate approaches to data analysis.
Students are able to interpret results and to discuss limitations.Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 L4, 1, room 004 OPM 801: Optimization and Heuristics8 ECTSLecturer(s)
Course Type: elective courseCourse Number: OPM 801Credits: 8Course Content
This course aims at Ph.D. students in information systems, business administration, and computer science. It provides a basic understanding of linear and mixed-integer optimization models and solution methods. The course is partly taught in a seminar-style format. Allocation of topics will be done together in the class.
Form of assessment: Assignment, Presentation, Class Participation
Competences acquired
The course aims to introduce the students to fundamental linear and combinatorial optimization problems. They learn to formulate optimization models as mixed-integer linear programs, how to solve them with standard software, and how to construct heuristic solution algorithms. The students learn to deal with the complexity of real-world problems via aggregation, relaxation, and decomposition techniques.
Schedule
Lecture Lecture 06.09.23 – 06.12.23 Wednesday 15:30 – 18:45 SO 322 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.
Form of assessment: Written elaboration 40 %, presentation 40 %, class participation 20 %
Competences acquired
Students will get a fundamental understanding of problems, theory and solution methods in nonlinear optimization. This includes to learn how to formulate a nonlinear optimization problem mathematically, how to analyze its structure to detect e.g. convexities, how to implement and solve a problem with state-of-the-art modeling environments and solvers. Students can bring in and work on their own problems of interest, e.g. a specific one that they might face in their thesis or an actual standard problem often encountered in practice
Schedule
Lecture Lecture 08.09.23 – 08.12.23 Friday 10:15 – 13:30 SO 322 OPM 918: Business Analytics: Models, Methods, Managerial Insights8 ECTSLecturer(s)
Course Type: elective courseCourse Number: OPM 918Credits: 8Course Content
This elective course aims at PhD students in Operations. The course is taught in a seminar-style format. Each student gives three presentations about one own research project based on a draft of a paper. The aim is to discuss and sharpen the contributions of that work. The presentations are structured similar to papers in that field:
1. Models: Problem description, Model formulations, and contributions to scientific literature
2. Methods: Analytical or algorithmic approaches
3. Managerial Insights: Structured properties, data analysis, and numerical results
Students act as discussants to presentations of other students. At the end of the seminar students hand in a draft of the paper, which reflects the discussions to each single point.
Form of assessment: Presentations during the course (60%), active contribution to class discussion (15%), draft of paper (25%)
Competences acquired
Students will learn how to structure and discuss their own research results for a presentation and for a paper. They will become acquainted with acting as discussant for other topics. They will learn how to identify and sharpen the contributions of their own work. They learn how to present the analysis of data and how to design numerical studies.
Schedule
Lecture Lecture 08.09.23 – 08.12.23 Friday 10:15 – 13:30 So 318 RES (Bridge course): Lecture series “Data Science in Action”5 ECTSCourse Type: elective courseCourse Number: RES (Bridge course)Credits: 5Course Content
Our colleagues from the Mannheim Center for Data Science are offering a lecture series “Data Science in Action” for the upcoming fall term. The lecture series is online and starts on Thursday, 14 September 2023 at midday.
GESS doctoral students can attend the event as a bridge course. In order to receive the 5 ECTS points, you need to take part in at 80% of the lectures and write a 15 page essay (pass/fail assessment).
For more information and registration, please visit the website:
https://www.uni-mannheim.de/en/datascience/details/vortragsreihe-data-science-in-action/
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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 Lecture 07.09.23 – 07.12.23 Thursday 12:00 – 13:30 EO 169 ACC / TAX 910: Area Seminar Accounting and TaxationECTSLecturer(s)
Course Type: core courseCourse Number: ACC / TAX 910Course 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.
Schedule
Seminar Seminar 04.09.23 – 04.12.23 Monday 15:30 – 17:00 O 048 Seminar 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 SO 318 ACC / TAX 916: Applied Econometrics I8 ECTSLecturer(s)
Course Type: core courseCourse Number: ACC / 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 Lecture 04.09.23 – 04.12.23 Monday 08:30 – 10:00 O226–28 Lecture 06.09.23 – 06.12.23 Wednesday 08:30 – 10:00 SO318 ACC / TAX 920: Brown Bag SeminarECTSLecturer(s)
Course Type: core courseCourse Number: ACC / TAX 920Course 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.
Coursedates will be announced via email to registered participants.
Competences acquired
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.
Schedule
Seminar Seminar 06.09.23 – 13.09.23 Wednesday 13:45 – 15:15 O 226–28 Seminar 20.09.23 – 06.12.23 Wednesday 13:45 – 17:00 O 048 E 701: Advanced Microeconomics I (for Business)8 ECTSLecturer(s)
Yasmin Hoffmann
Course Type: core courseCourse Number: E 701Credits: 8Prerequisites
Mathematics for Economists, intermediate knowledge of microeconomics
Course Content
Please note that course times are not final at this point! More exercises will be added soon!
The course is aimed at doctoral students in all fields of business administration. It provides an introduction to microeconomic theory on a graduate level, highlighting aspects which are of specific relevance for business research.
The main topics covered include:
- Theory of consumer choice under certainty and uncertainty
- Theory of the firm, production cost and supply
- Markets, equilibrium, 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.
Form of assessment: Midterm and final exams: 80–100%, Exercises: Up to 20%
The course is also part of the TRR 266 Accounting for Transparency
Competences acquired
Understanding and critically evaluating the fundamental concepts of microeconomic theory, game theory and mechanism design; learning the relevant tools and underlying assumptions for economic analysis in ongoing research.
Schedule
Lecture Lecture 05.10.23 – 05.10.23 Thursday 13:45 – 15:15 O 226–28 Lecture 10.10.23 – 05.12.23 Tuesday 15:30 – 17:00 O 129 Lecture 12.10.23 – 07.12.23 Thursday 13:45 – 15:15 O 145 Lecture 12.10.23 – 30.11.23 Thursday 15:30 – 17:00 EO 145 Lecture 13.10.23 – 13.10.23 Friday 15:30 – 17:00 EO 242 Exam 08.11.23 – 08.11.23 Wednesday 10:15 – 11:45 B 243 Lecture 20.11.23 – 20.11.23 Monday 13:45 – 15:15 O 135 Tutorial Exercise 18.10.23 – 06.12.23 Wednesday 10:15 – 11:45 B6, 30–32 (E-F), room 211 Exercise 02.11.23 – 02.11.23 Thursday 13:45 – 15:15 O 145 Exercise 07.11.23 – 07.11.23 Tuesday 15:30 – 17:00 O 129 E 703: Advanced Econometrics I (for Business)8 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: Matrix Algebra and Probability Theory, Ordinary Least Squares, Maximum Likelihood, Generalized Method of Moments, 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
Teaching Assistant: Richard Winter Laura Arnemann
Grading: Written Exam (120 min) 90 %, problem sets 10 %
Please note: The course starts in October. The additional September sessions are voluntary refreshers for those students who do not have to take E700 Mathematics for Economists
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 10:15 – 11:45 SO 318 Lecture 07.09.23 – 07.12.23 Thursday 10:15 – 11:45 O048 Tutorial Exercise 06.09.23 – 06.12.23 Wednesday 15:30 – 17:00 257 (L7, 3–5) Exercise 08.09.23 – 08.12.23 Friday 10:15 – 11:45 O 226–28 E700: Mathematics for Economists (1st year)6 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)
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
So Jin Lee and Chang Liu
Schedule
Lecture Lecture 04.09.23 – 25.09.23 Monday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 05.09.23 – 26.09.23 Tuesday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 06.09.23 – 27.09.23 Wednesday 10:15 – 11:45 L7, 3–5 Room 001 Lecture 07.09.23 – 28.09.23 Thursday 10:15 – 11:45 L7, 3–5 Room 001 Exam 06.10.23 – 06.10.23 Friday 08:00 – 10:00 Palace, Room SN 163 Retake exam 04.12.23 – 04.12.23 Monday 10:00 – 12:00 B 6, 30–32, E-F, Room 212 Tutorial Group 1 04.09.23 – 25.09.23 Monday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 04.09.23 – 25.09.23 Monday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 04.09.23 – 25.09.23 Monday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 04.09.23 – 25.09.23 Monday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 Group 1 05.09.23 – 26.09.23 Tuesday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 05.09.23 – 26.09.23 Tuesday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 211 Group 3 05.09.23 – 26.09.23 Tuesday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 05.09.23 – 26.09.23 Tuesday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 211 Group 1 06.09.23 – 27.09.23 Wednesday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 06.09.23 – 27.09.23 Wednesday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 06.09.23 – 27.09.23 Wednesday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 06.09.23 – 27.09.23 Wednesday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 Group 1 07.09.23 – 28.09.23 Thursday 13:45 – 15:15 L 7, 3–5 Room P044 Group 2 07.09.23 – 28.09.23 Thursday 13:45 – 15:15 B6, 30–32 (E-F), Seminar room 310 Group 3 07.09.23 – 28.09.23 Thursday 15:30 – 17:00 L7, 3–5 Room P044 Group 4 07.09.23 – 28.09.23 Thursday 15:30 – 17:00 B6, 30–32 (E-F), Seminar room 310 ACC 923: Corporate Sustainability and Decarbonization3 ECTSLecturer(s)
Course Type: elective courseCourse Number: ACC 923Credits: 3Course Content
This seminar is aimed at doctoral students at GESS. The seminar hosts speakers from academia and industry to discuss latest advances and challenges that companies face in the transition towards more sustainable business practices and net carbon emissions of zero. Topics covered include the economics and management of sustainability activities and emission abatement strategies across all sectors of the economy.
Course participants need to attend the seminar talks and the internal sessions. In the internal sessions, students are asked to present a paper and/
or take the role of a discussant. Readings may additionally include recent theory or empirical papers. Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on the covered topics and to identify promising avenues for future research. Moreover, students receive a training on how to present and evaluate papers in seminars and conferences.
Form of assessment: Participation (20%), Paper presentations and discussions (80%)
Schedule
Lecture Lecture 04.09.23 – 04.12.23 Monday 17:15 – 18:45 O 129 IS 808: Advanced Data Science Lab I (Network Science)6 ECTSLecturer(s)
Course Type: elective courseCourse Number: IS 808Credits: 6Course Content
The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing relational data, for example complex phenomena and systems, using techniques and methods from the domain of network science. The students will present their results and write a paper about their research.
Assessment: Term Paper 90% and Presentation 10%
Competences acquired
Students will be equipped with practical experience with conducting scientific data-science projects. They will train their presentation skills, learn to communicate in research projects and receive feedback.
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 17:00 L15, 1–6, 314–315 MET: Applied Data Science: Machine Learning for Economics and Business Data5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
The reading course is aimed at Ph.D. students in or beyond their second year to support them during their research phase. 1st year PhD students are welcomed to attend the class as well.
Recommended: Knowledge of basic statistics and prior experience with Python, R or Stata is helpful, but not necessary.
Course Content
This course provides a gentle introductory and practical approach to understanding and analyzing data using machine learning and artificial intelligence techniques with applications for economics and business data. Why these methods are an asset in the toolkit for students is clear: Machine learning methods may outperform conventional econometric methods in applications where data needs to be collected and classified or outcomes to be predicted. But the ultimate goal of economic analysis is often causal inference. We evaluate in which cases the promises of machine learning methods may enhance causal inference and at what cost incurred by imposing additional structure. We systematically review in the first part of the course supervised machine learning methods and artificial intelligence methods and benchmark them to conventional econometrics. In the second part, we discuss how to apply the techniques for causal inference and solve practical problems. The applications give plenty of opportunities to learn how to use the programming language Python. We will use the German Business Panel linked to large-scale datasets as a running example and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes.
In each session, one of the participants will present a method based on research papers and/
or program code, which we will discuss in light of real-world programming applications. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic. In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to apply the introduced methods or assess the results of the applications.
Learning outcomes: The specific applications cover a broad set of skills with a focus on application of machine learning and artificial intelligence techniques, analysis of big and unstructured data, classification, inference, writing of own program codes and reports, and opportunities for own research.
- Analytical Skills/
Problem-Solving: Students will effectively visualize, conceptualize, articulate, and solve or address problems, with available or newly generated information, through experimentation and observation, mainly using statistical and programming tools. - Critical Thinking: Students will apply empirical analysis to everyday problems in data collection and analysis helping them to understand events, evaluate specific methods, compare arguments with different conclusions to a specific issue, and assess the role played by assumptions.
- Quantitative Reasoning: Students will understand how to design collection and analysis of empirical evidence. Specifically, they may obtain and/
or collect relevant data, develop empirical evidence using appropriate statistical techniques, and interpret the results of such analyses. - Specialized Knowledge and Practical Application: Students will develop deeper analytical, critical, and quantitative skills in specialized areas by applying programming skills and statistical concepts to real world situations.
- Interdisciplinary Knowledge: Participants will broaden their knowledge by studying methods used in economics, sociology, political science, and other fields.
- Communication and Leadership: Participants will build presentation and discussion skills, ensuring they are prepared to navigate diverse audiences and situations. Collaborations of participants prepares joint projects.
- Preparation of Own Research: Projects will be valuable for own research projects; applications provide best practice examples.
Form of assessment: Paper (technical report) (50 %), Presentation (25 %), Class Participation (25 %)
The course is also part of the TRR 266 Accounting for Transparency
Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 15:30 – 17:00 O 226–28 MET: Intensive Longitudinal Methods in Contexts of Work and Learning5 ECTSLecturer(s)
Course Type: elective courseCourse Number: METCredits: 5Prerequisites
Basic knowledge of statistics; basic experience with the statistic software R.
Prior course registration is required and regular attendance is expected.
Preferably, the participants are planning a study with intensive longitudinal methods in the context of work and learning or have already conducted such a study.
Course Content
Content:
- research designs and research questions for intensive longitudinal methods (ILM)
- methods of data collection (diary methods, experience sampling, ecological momentary assessment, etc.)
- methods of data analysis (esp. multilevel analysis), and limitations
Competences acquired
Students are able to develop and evaluate research designs including ILM.
Students are able to develop and evaluate methods of data collection.
Students are able to distinguish and apply an adequate approaches to data analysis.
Students are able to interpret results and to discuss limitations.Schedule
Lecture Lecture 05.09.23 – 05.12.23 Tuesday 13:45 – 15:15 L4, 1, room 004 RES (Bridge course): Lecture series “Data Science in Action”5 ECTSCourse Type: elective courseCourse Number: RES (Bridge course)Credits: 5Course Content
Our colleagues from the Mannheim Center for Data Science are offering a lecture series “Data Science in Action” for the upcoming fall term. The lecture series is online and starts on Thursday, 14 September 2023 at midday.
GESS doctoral students can attend the event as a bridge course. In order to receive the 5 ECTS points, you need to take part in at 80% of the lectures and write a 15 page essay (pass/fail assessment).
For more information and registration, please visit the website:
https://www.uni-mannheim.de/en/datascience/details/vortragsreihe-data-science-in-action/
TAX 923: Reading Course Taxation Research5 ECTSLecturer(s)
Course Type: elective courseCourse Number: TAX 923Credits: 5Course Content
The course provides a forum to discuss recent state-of-the art papers in taxation research (mostly applied empirical). All covered papers are recently published or in the working paper stage. In each class session, one student briefly presents a research paper before the paper is discussed in class. All students are expected to read the research paper to be discussed in preparation for the class and it is one main objectives of the course that papers are lively discussed among all class participants.
Students can choose papers which they wish to present or the responsible instructors provide a selection from which to pick. Students are encouraged to choose papers which are on the reading list for their thesis. The course could also serve as a forum for discussing paper drafts of peers or researchers within the network.
In addition to presenting a paper in class, students are expected to write a referee report for a research paper. This will teach how to evaluate a paper critically and how to write a referee report.
The reading course is particularly aimed at 2nd and higher year Ph.D. students to support them during their research phase. 1st year PhD students are welcomed to attend the class as well. Students can attend and earn credits for both this class as well as the related class TAX 922 (which is taught in the spring semester).
Form of assessment: Paper (referee report) (40 %), Presentation (30 %), Class Participation (30 %)
Competences acquired
- Know your field and related fields: Learn about the literature, both in your own (sub-field) of interest and other fields.
- Commit to a reading routine for your thesis
- Community building: The reading group will spawn discussion and encourage community building
- Ability to present and confidence building: Learn how to present well. (This is often easier with a paper that somebody else wrote – one is not as emotionally involved in the question/
approach/ results as with one’s own paper.) - Discussion competence: Learn how to be a good seminar participant: Behave well, ask clear questions, discuss in an appropriate manner etc.
- Ability to understand: Learn how to read and approach research papers and learn to summarize the main message/
points of the paper - Participation in scientific discourse
- Learn how to evaluate a paper critically
- Writing a referee report
Schedule
Lecture Lecture 04.09.23 – 04.12.23 Monday 10:15 – 11:45 O 048