Spring 2021

  • Accounting

    ACC / TAX 910: Area Seminar Accounting and Taxation
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: ACC / TAX 910
    Course Content

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

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

    Form of assessment: Oral participation.


    Seminar Dates are announced here.

    ACC / TAX 920: Brown Bag Seminar
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: ACC / TAX 920
    Course Content

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

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

    Form of assessment: Class Participation


    Coursedates will be announced via email to registered participants.

    ACC 903: Empirical Accounting Research I (Research Methods)
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: ACC 903
    Credits: 6
    Course Content

    This course provides a comprehensive overview of research topics and core methods in influential seminal as well as contemporaneous papers in the empirical accounting literature. In particular, we cover after an (1) introduction and a review of some “Accounting Classics”, the literatures on (2) Earnings Management, (3) Valuation (value relevance, earnings response coefficients (ERC)/event studies, accounting-based valuation), (4) Voluntary Disclosure, (5) Mandatory Disclosure, (6) International/-Institutional Accounting and IFRS, (7) Corporate Narratives, and (8) Bank Accounting. In each session, there is first an overview lecture introducing core methods in the corresponding field, and second a session in which we will jointly discuss in more depth selected empirical methods. Students are expected to prepare all readings and to lead selected class discussions about assigned papers in the second half of each lecture.

    The lectures and student discussions are supplemented by exercise sessions in which we discuss broader related topics such as which fields are currently ‘en vogue’ in the journals, how to ‘stay informed’ and identify potentially relevant regulatory changes, how to know about topics influential researchers are currently working on, or discuss where students see their individual strength and how they can become competitive researchers in the future.

    Learning outcomes: Students should know about the core issues of existing accounting research and established empirical research methodologies. They should also be able to place current research into the literature and to critically evaluate its relevance and technical rigor, and therefore to develop meaningful research ideas to extend current knowledge.

    Form of assessment: Exam (90 minutes) 50 %, paper presentations and exercise sessions 50 %


     

    Schedule
    Lecture
    02.03.21 – 27.04.21 Tuesday 15:30 – 18:30 O 048/050 + online
    ACC 904: Empirical Accounting Research II (Causal Inference)
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: ACC 904
    Credits: 6
    Course Content

    The course provides PhD students with an introduction to current topics and methods in empirical accounting research.  The course aims to survey a wide variety of empirical research in in this field.  The course covers methodological issues, theoretical background, and selected empirical papers.  The assigned papers serve as examples to illustrate challenges of empirical research in accounting.

    The course is structured around different identification approaches that are frequently used in recent accounting research.  This structure reflects the increasing importance of empirical strategies to address causality concerns in empirical accounting papers.  Since research in econ and finance took a lead on these questions, a number of examples from these fields will be integrated into the course.

    Learning outcomes: Students are able to understand and evaluate research questions, contribution, and research methods of current research papers on accounting-related issues. Students are also able to develop new research questions based on their knowledge of the accounting literature.

    Form of assessment: Exam (90 minutes) 50 %, presentation 50 %


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

    Schedule
    Lecture
    01.03.21 – 03.05.21 Monday 12:00 – 15:15 O 135 + online
    ACC 921: Topics in Analytical Accounting Research
    8 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: ACC 921
    Credits: 8
    Course Content

    This course is aimed at doctoral students in accounting and neighboring fields including economics, finance, political and operations management. The course seeks to provide an introduction to the role of accounting information in (a) measuring cost and profitability, (b) accounting-based managerial performance measures, (c) cost allocation and internal pricing in multi-divisional firms and (d) financial ratios and firm valuation.

    • The main topics covered in this course include:
    • Measures of Product Cost and Relevant Costs
    • Measures of Profitability
    • Managerial Performance Evaluation
    • Internal Pricing
    • Accounting-based Equity Valuation and Financial Ratios

    Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on these topics and to identify promising avenues for future research. The course readings include recent theoretical and empirical papers.

    Form of assessment: Homework Assignments (30%), Class Participation (15%), Paper Presentation (15%), Final Examination (40%)

     

    Schedule
    Lecture
    04.05.21 – 15.06.21 Tuesday 15:30 – 17:00 O 151 + online
    06.05.21 – 17.06.21 Thursday 15:30 – 17:00 O 151 + online
    IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: IntRes
    Credits: 5
    Course Content

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll.

    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. Mini Research Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to the other participants. You will give a presentation on a current working paper or research project of yours and you will discuss a paper/presentation from one of your fellow students from another field (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in two weeks after the presentation.

     

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in the ‘Mini Research Day’. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant 10 days before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first serve basis.


    Course dates:

    February 15th, 2021 – Course Registration Deadline

    March 11th, 2021, 12:30pm-3:30pm, Kick-Off Meeting         

    April 12th, 2021 – Deadline to send paper to discussant (and in cc: to gess@uni-    mannheim.de)

    April 22th, 2021 – Mini Research Day (whole day symposium)

    April 28 th, 2021 – time TBA – Science Speed Dating Event

    May 28th, 2021, Presentation of research proposal (half- to full day symposium)

    June 13th, 2021 – Deadline to hand in interdisciplinary research proposal (to:gess uni-mannheim.de)

    MET: Interpretability and Fairness in Machine Learning
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Prior experience with R is helpful, but not necessary. Optional learning resources can be found here: https://rstudio.cloud/learn/primers

    Course Content

    Machine Learning (ML) is increasingly used to guide high-stakes decisions in various contexts such as college admissions, granting loans or hiring employees. By eliminating human judgment, ML-based decision-making promises to be neutral and objective and to find the right decisions in shorter time. At the same time, however, concerns are raised that algorithmic decision-making may foster discrimination and amplify existing biases that are fed into the models. This course discusses recent advances in the field of Interpretable and Fair ML: How can we explain predictions of black-box models? How can we measure and mitigate biases to make ML models fair? In addition to covering fairness and interpretability, the course will include a general introduction to supervised machine learning. Hands-on lab sessions will demonstrate how to train and interpret ML models using R.

    Course requirements & assessment:

    Presentation and  term paper (graded)

    Schedule
    Seminar
    1st group 11.03.21 – 17.06.21 Thursday 13:45 – 15:15 Sowi Zoom 03 Link
    2nd group 16.04.21 – 28.05.21 Friday 12:00 – 15:15 Sowi Zoom 04 Link
  • Finance

    FIN 620: Behavioral Finance
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: FIN 620
    Credits: 6
    Course Content

    This course is for participants of FIN 901 only.

    There is abundant evidence suggesting that the standard economic paradigm of rational investors does not adequately describe behavior in financial markets. Behavioral Finance examines how individuals' attitudes and behavior affect their financial decisions. This course reviews recent research on possible mispricing in financial markets due to the nature of psychological biases. Moreover the course deals with behavioral finance models explaining investor behavior or market anomalies when rational models provide no sufficient explanations. Topics will include among others overconfidence, prospect theory, heuristic driven biases and frame dependence.

    Learning outcomes: Behavioral finance applies scientific research on human and social cognitive and emotionalbiases. After completing this course, students will be able to better understand economic decisions and how they affect market prices and returns. They will know how behavioral findings are integrated with neo-classical theory.

    Form of assessment: Written exam (60 min.)

    Schedule
    Lecture
    04.03.21 – 25.03.21 Thursday 08:30 – 11:45 online
    15.03.21 Monday 17:15 – 20:30 online
    22.03.21 Monday 11:15 – 20:30 online
    Tutorial
    03.03.21 – 28.04.21 Wednesday 12:00 – 13:30 online
    FIN 802: Continuous Time Finance
    8 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: FIN 802
    Credits: 8
    Prerequisites

    FIN 801 Discrete-Time Finance

    Course Content

    Itô calculus, stochastic differential equations, Black-Scholes theory, hedging and arbitrage pricing of European, American, and exotic options, complete and incomplete market models, consumption investment problems, term structure theory for volatility and interest rates, default risk

    Learning outcomes: The course aims at providing the basic concepts and techniques for modelling and analysing financial price processes in continuous time.

    Form of assessment: Term paper 90 %, participation 10 %

    Schedule
    Lecture
    09.03.21 Tuesday 10:00 – 18:00 online
    23.03.21 Tuesday 10:00 – 18:00 online
    30.03.21 Tuesday 10:00 – 13:00 online
    18.05.21 Tuesday 09:00 – 18:00 online
    FIN 803: Corporate Finance
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: FIN 803
    Credits: 6
    Prerequisites

    Formal: E 701, E 703, FIN 801

    Recommended:

    • A first-year doctoral level course in microeconomics that covers game theory and information economics (signaling, adverse selection, equilibrium refinements)
    • A first-year doctoral level course in econometrics that covers estimation and testing theory.
    • Some familiarity with corporate finance and financial institutions at the level of a masters level course is also assumed, but not essential. If you have no prior knowledge of corporate finance, then some chapters in an MBA-level textbook (e.g. Brealey, Myers, and Allen, Principles of Corporate Finance, 11th edition, McGraw Hill 2013; Berk and DeMarzo, Corporate Finance, 3rd edition, Pearson 2013) would be useful.
    Course Content

    This course is intended to enable students to understand and conduct research in corporate finance. It is taught at a first-year doctoral level.

    Learning outcomes: The course combines two objectives. Firstly, participants learn the classic contributions to the theory of modern corporate finance and understand the main contributions to the field. Secondly, the course also introduces some of the main empirical contributions to the field and studies the main econometric and statistical techniques used in corporate finance. At the end of the course participants should be familiar with the main empirical and theoretical tools used in corporate finance.

    Form of assessment: Essay (take-home exam)

    Exam: 1 June 2021, 10 a.m.

    Schedule
    Lecture
    12.03.21 Friday 08:30 – 12:30 online
    26.03.21 Friday 08:30 – 12:30 online
    16.04.21 Friday 08:30 – 12:30 online
    30.04.21 Friday 08:30 – 12:30 online
    14.05.21 Friday 08:30 – 12:30 online
    28.05.21 Friday 08:30 – 12:30 online
    FIN 901: Behavioral Finance
    2 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: FIN 901
    Credits: 2
    Course Content

    FIN 901 is a continuative course of FIN 620. In this course students discuss and present current research topics in behavioral finance.

    Learning outcomes: Students learn to critically discuss current research papers, i.e. data, methodology, and reasoning.

    Form of assessment: Presentation

    Schedule
    Lecture
    Kick-off 04.03.21 Thursday 11:45 – 13:00 online
    FIN 910: Area Seminar Finance
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: FIN 910
    Course Content

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

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

    Form of assessment: Oral participation.


    Seminar Dates are announced here.

    IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: IntRes
    Credits: 5
    Course Content

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll.

    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. Mini Research Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to the other participants. You will give a presentation on a current working paper or research project of yours and you will discuss a paper/presentation from one of your fellow students from another field (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in two weeks after the presentation.

     

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in the ‘Mini Research Day’. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant 10 days before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first serve basis.


    Course dates:

    February 15th, 2021 – Course Registration Deadline

    March 11th, 2021, 12:30pm-3:30pm, Kick-Off Meeting         

    April 12th, 2021 – Deadline to send paper to discussant (and in cc: to gess@uni-    mannheim.de)

    April 22th, 2021 – Mini Research Day (whole day symposium)

    April 28 th, 2021 – time TBA – Science Speed Dating Event

    May 28th, 2021, Presentation of research proposal (half- to full day symposium)

    June 13th, 2021 – Deadline to hand in interdisciplinary research proposal (to:gess uni-mannheim.de)

    MET: Interpretability and Fairness in Machine Learning
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Prior experience with R is helpful, but not necessary. Optional learning resources can be found here: https://rstudio.cloud/learn/primers

    Course Content

    Machine Learning (ML) is increasingly used to guide high-stakes decisions in various contexts such as college admissions, granting loans or hiring employees. By eliminating human judgment, ML-based decision-making promises to be neutral and objective and to find the right decisions in shorter time. At the same time, however, concerns are raised that algorithmic decision-making may foster discrimination and amplify existing biases that are fed into the models. This course discusses recent advances in the field of Interpretable and Fair ML: How can we explain predictions of black-box models? How can we measure and mitigate biases to make ML models fair? In addition to covering fairness and interpretability, the course will include a general introduction to supervised machine learning. Hands-on lab sessions will demonstrate how to train and interpret ML models using R.

    Course requirements & assessment:

    Presentation and  term paper (graded)

    Schedule
    Seminar
    1st group 11.03.21 – 17.06.21 Thursday 13:45 – 15:15 Sowi Zoom 03 Link
    2nd group 16.04.21 – 28.05.21 Friday 12:00 – 15:15 Sowi Zoom 04 Link
  • Information Systems

    IS / OPM 910: Area Seminar Information Systems and Operations Management
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: IS / OPM 910
    Course Content

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

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

    Form of assessment: Oral participation.


    Seminar Dates are announced here.


    IS 807: Designing Qualitative Research Projects
    9 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: IS 807
    Credits: 9
    Course Content

    This course provides an overview of qualitative research methods and their application in the field of Information Systems (IS). The course begins with an introduction to the basic principles and alternatives of conducting qualitative research. It then provides deeper insights into different types of qualitative research in terms of their epistemological, ontological, and methodological stance. For each stance, the underlying principles will be discussed with illustrative examples. The course is taught in a seminar style, requiring students to prepare readings before class and discuss them in class. Student presentations will help elicit the strengths and weaknesses of different qualitative methods in published research. Overall, the course is designed to be interactive. For a final paper, the students illustrate the application of particular methods to design their own qualitative research study.

    Learning outcomes: After completing the course, students

    • have gained an overview of the common qualitative methods in IS research
    • know quality criteria and corresponding means to improve qualitative research designs
    • are able to prepare, plan, and execute own qualitative research endeavors.

    Form of assessment: Term paper 50%, presentation 30 %, discussion 20 %

    Schedule
    Lecture
    03.03.21 – 05.05.21 Wednesday 12:00 – 15:00 online
    IS 903: Information Systems Theories
    8 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: IS 903
    Credits: 8
    Course Content

    Knowledge creation and dissemination are key objectives of scientific endeavors. However, what constitutes knowledge is a highly contested issue. Certainly, at the core of social science disciplines, knowledge is inseparable from theory. Indeed, to seek theory-guided explanations of real-world phenomenon is what separates scholars from consultants, who seek to change reality without explaining it, and from journalists, who report reality but do not explain it. The pursuit of theory drives us to understand reality—to discover truth—before making recommendations on how to change reality. To pursue theory is to pursue knowledge; to pursue knowledge is to advance humanity. Consequently, many scholars emphasize the centrality of theories for any scientific endeavor—a thought widely reflected in many disciplines from the natural to the social sciences. While attention to theoretical work has been at the heart of the Information Systems (IS) discipline for a long time, the focus on theoretical debates and genuine conceptual contributions has been picking up recently. This is reflected by a number of journal sections and conference tracks dedicated to advancing theory and theorizing in IS research just as much as in many authors’ experiences during the reviews of their work.

    This course invites participants to join the ongoing discourse on theories and theorizing in the IS research community. It is designed to help participants build and extend their understanding of the nature and role of theory in IS research. Through discussions and analyses of current theoretical developments in the IS discipline and some of its main reference disciplines, participants will engage with theory and advance their skills of crafting their own theoretical contributions and evaluating those of others.

    Learning outcomes:

    • Understand the importance and usefulness of theory in research
    • Learn theorizing strategies
    • Learn to evaluate theoretical contribution in research
    • Develop basic theorizing skills
    • Identify a theory that could be applicable to the participants’ own research programs

    Form of assessment: Term paper 60%, presentation 20%, discussion 20%

    Schedule
    Lecture
    03.03.21 – 16.06.21 Wednesday 10:25 – 11:45 online
    IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: IntRes
    Credits: 5
    Course Content

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll.

    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. Mini Research Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to the other participants. You will give a presentation on a current working paper or research project of yours and you will discuss a paper/presentation from one of your fellow students from another field (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in two weeks after the presentation.

     

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in the ‘Mini Research Day’. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant 10 days before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first serve basis.


    Course dates:

    February 15th, 2021 – Course Registration Deadline

    March 11th, 2021, 12:30pm-3:30pm, Kick-Off Meeting         

    April 12th, 2021 – Deadline to send paper to discussant (and in cc: to gess@uni-    mannheim.de)

    April 22th, 2021 – Mini Research Day (whole day symposium)

    April 28 th, 2021 – time TBA – Science Speed Dating Event

    May 28th, 2021, Presentation of research proposal (half- to full day symposium)

    June 13th, 2021 – Deadline to hand in interdisciplinary research proposal (to:gess uni-mannheim.de)

    IS 911: Context-Aware Computing
    8 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: IS 911
    Credits: 8
    Prerequisites

    Fundamentals in computer science and programming

    Course Content

    This course covers principles and foundations of context-aware computing. Approaches to context acquisition, reasoning and management are presented and current trends in research are discussed.

    Learning outcomes: Students will gain foundational knowledge about context-aware computing. They will learn about modelling and using context in computing systems. This includes context management and reasoning. After this module, students will know about the current state of the art in context-aware computing.

    Form of assessment: Assignment 40 %, Discussion 40 %, Class Participation 20 %

    Schedule
    Lecture
    02.03.21 – 15.06.21 Tuesday 13:45 – 15:15 online
    MET: Interpretability and Fairness in Machine Learning
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Prior experience with R is helpful, but not necessary. Optional learning resources can be found here: https://rstudio.cloud/learn/primers

    Course Content

    Machine Learning (ML) is increasingly used to guide high-stakes decisions in various contexts such as college admissions, granting loans or hiring employees. By eliminating human judgment, ML-based decision-making promises to be neutral and objective and to find the right decisions in shorter time. At the same time, however, concerns are raised that algorithmic decision-making may foster discrimination and amplify existing biases that are fed into the models. This course discusses recent advances in the field of Interpretable and Fair ML: How can we explain predictions of black-box models? How can we measure and mitigate biases to make ML models fair? In addition to covering fairness and interpretability, the course will include a general introduction to supervised machine learning. Hands-on lab sessions will demonstrate how to train and interpret ML models using R.

    Course requirements & assessment:

    Presentation and  term paper (graded)

    Schedule
    Seminar
    1st group 11.03.21 – 17.06.21 Thursday 13:45 – 15:15 Sowi Zoom 03 Link
    2nd group 16.04.21 – 28.05.21 Friday 12:00 – 15:15 Sowi Zoom 04 Link
  • Management

    MAN 801: Advances in Entrepreneurship and Management Research
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MAN 801
    Credits: 6
    Course Content

    Besides focusing on selected topics revolving around entrepreneurship, this Ph.D. course will emphasize various topics in organization theory. Well-known researchers from both fields will provide introductions into and overviews of the state of the art of scholarship in these two domains. The organization theories include Max Weber’s Theory of Bureaucracy, Taylorism and its actual descendants, theories of Organizational Evolution, Behavioral Theories of Organization (concentrating on the “Carnegie School”), Neoinstitutional Theory and Network Theory. In the entrepreneurship part theories comprise sociological and psychological approaches as well as economics of entrepreneurship.

    Learning outcomes: The course aims at enabling students to understand basic concepts in organization theory and entrepreneurship research, identify appropriate theoretical concepts and lenses and apply them properly to their individual research topics.

    Form of assessment: 20 min. presentation of a theory from the perspective of an interested newcomer as well as a) Paper which discusses the application of an organization theory to a self-chosen organizational problem, or b) develop a research proposal for a selected question in entrepreneurship theory. Essay 80 %, presentation 20 %.

    Schedule
    Lecture
    Kick-Off 24.03.21 Wednesday 15:30 – 17:30 online
    21.04.21 – 09.06.21 Wednesday 15:30 – 17:30 online
    MAN 804: Advances in Strategic Managment
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MAN 804
    Credits: 6
    Course Content

    The seminar serves the purpose of familiarizing doctoral students with the most relevant research streams and trends in strategy research. We will read and discuss current state-of-the-art research with a special focus on the recent scholarly debate in the “Strategic Management Journal”, we will reflect the most prevalent theoretical lenses, key subject areas and phenomena as well as the empirical designs applied by scholars in this particular domain. Moreover, we will discuss the art of article writing for dedicated field journals.

    Learning outcomes:

    • Develop an understanding of the most established as well as the latest emerging literature substreams in strategy research.
    • Gain an overview of the most prevalently studied phenomena and subject areas in these literature substreams.
    • Become familiar with the theoretical and methodological approaches used to address the different sets of research questions.

    Form of assessment: Essay 60 %, presentation 40 %

    Schedule
    Lecture
    10.03.21 Wednesday 13:00 – 14:30 online
    26.04.21 Monday 09:00 – 17:00 online
    27.04.21 Tuesday 09:00 – 17:00 online
    MAN 807: Experimental Research in Management
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MAN 807
    Credits: 6
    Course Content

    Gary Loveman, former Chief Executive Officer and President of Caesars Entertainment Corporation: “There are three things that can get you fired from Caesars: Stealing, sexual harassment, and running an experiment without a control group.”

    This course provides an introduction to the fundamental methodological issues that arise in experimental and quasi-experimental research. Illustrative examples are drawn from the behavioral sciences with a focus on the behavior of consumers and employees. Topics that are covered include: the development of research ideas; data collection and reliable measurement procedures; threats to validity; control procedures and experimental designs; and data analysis. Emphasis is placed on attaining a working knowledge of the use of regression and analysis of variance methods for experimental data.

    Participants develop their own idea for an experimental study that will be presented, discussed, and developed in class. Participants write a short research paper summarizing the research idea, the methodological approach, as well as the expected contribution of the results.

    Learning outcomes: Through participating the in course, participants will:

    • Train their scientific writing and presentation skills, and receive feedback concerning their research ideas;
    • Gain an understanding of methodological issues that arise in experimental and quasi-experimental research;
    • Attain the skills needed to plan and execute their own experiments and analyze the resulting data.

    Form of assessment: Term paper (incl.final presentation)

    Schedule
    Lecture
    15.04.21 Thursday 10:15 – 11:45 online
    22.04.21 Thursday 10:15 – 11:45 online
    29.04.21 Thursday 10:15 – 11:45 online
    06.05.21 Thursday 10:15 – 11:45 online
    20.05.21 Thursday 10:15 – 11:45 online
    10.06.21 Thursday 10:15 – 11:45 online
    MAN 910: Area Seminar Management
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MAN 910
    Course Content

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

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

    Form of assessment: Oral participation.


    Seminar Dates are announced here.

    IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: IntRes
    Credits: 5
    Course Content

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll.

    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. Mini Research Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to the other participants. You will give a presentation on a current working paper or research project of yours and you will discuss a paper/presentation from one of your fellow students from another field (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in two weeks after the presentation.

     

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in the ‘Mini Research Day’. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant 10 days before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first serve basis.


    Course dates:

    February 15th, 2021 – Course Registration Deadline

    March 11th, 2021, 12:30pm-3:30pm, Kick-Off Meeting         

    April 12th, 2021 – Deadline to send paper to discussant (and in cc: to gess@uni-    mannheim.de)

    April 22th, 2021 – Mini Research Day (whole day symposium)

    April 28 th, 2021 – time TBA – Science Speed Dating Event

    May 28th, 2021, Presentation of research proposal (half- to full day symposium)

    June 13th, 2021 – Deadline to hand in interdisciplinary research proposal (to:gess uni-mannheim.de)

    MET: Interpretability and Fairness in Machine Learning
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Prior experience with R is helpful, but not necessary. Optional learning resources can be found here: https://rstudio.cloud/learn/primers

    Course Content

    Machine Learning (ML) is increasingly used to guide high-stakes decisions in various contexts such as college admissions, granting loans or hiring employees. By eliminating human judgment, ML-based decision-making promises to be neutral and objective and to find the right decisions in shorter time. At the same time, however, concerns are raised that algorithmic decision-making may foster discrimination and amplify existing biases that are fed into the models. This course discusses recent advances in the field of Interpretable and Fair ML: How can we explain predictions of black-box models? How can we measure and mitigate biases to make ML models fair? In addition to covering fairness and interpretability, the course will include a general introduction to supervised machine learning. Hands-on lab sessions will demonstrate how to train and interpret ML models using R.

    Course requirements & assessment:

    Presentation and  term paper (graded)

    Schedule
    Seminar
    1st group 11.03.21 – 17.06.21 Thursday 13:45 – 15:15 Sowi Zoom 03 Link
    2nd group 16.04.21 – 28.05.21 Friday 12:00 – 15:15 Sowi Zoom 04 Link
  • Marketing

    MKT 804: Theory Development and Model Building
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MKT 804
    Credits: 6
    Course Content

    This course teaches students how to develop and test theories in an applied and concrete way. We discuss and study a range of research approaches and methods, including structural equation modeling and experiments. This course provides students with an opportunity to develop and to fine-tune appropriate and specific theories for their own research.

    Learning outcomes: Students come up and choose a specific topic of their interest in the beginning of the class and develop and present a theoretical framework suitable for their project. The latter part of the course is geared towards designing means to test the proposed theory. Another key learning outcome is to enhance students’ ability to conduct sound academic research and help them to derive hypotheses for their own research projects.

    Form of assessment: Project (40%), presentation (60%)

    Schedule
    Lecture
    24.03.21 Wednesday 13:45 – 15:15 online
    28.04.21 Wednesday 13:45 – 15:15 online
    05.05.21 Wednesday 13:45 – 15:15 online
    12.05.21 Wednesday 13:45 – 15:15 online
    19.05.21 Wednesday 13:45 – 17:00 online
    26.05.21 Wednesday 13:45 – 15:15 online
    02.06.21 Wednesday 13:45 – 17:00 online
    MKT 901: Designing Marketing Research Projects
    6 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MKT 901
    Credits: 6
    Course Content

    In this course, students will develop their own marketing research projects (e.g., as parts of their own dissertation projects). In presentation sessions, students will present their research project to all participants of the class and to the instructor. Discussions among partici­pants as well as the instructor’s feedback aim at strengthening and refining the positioning and the contribution of the individual projects. Students in the first year of their Ph.D. studies can thus use this course to get important insights for the preparation and refinement of their disser­ta­tion proposal.

    At the beginning of the course, objectives, general guidelines, and best practices for developing impactful research projects will be provided in a kick-off meeting. Furthermore, best practices how to get published in leading journals will be discussed. Then, students will start developing their projects. Students are not limited with respect to the choice of their individual research topic; however, it is made in accordance with the instructor.

    Students will prepare the project by developing a presentation containing the positioning and research questions, a brief literature review, the theoretical foundations and research hypotheses, as well as an outlook on potential methodological approaches (such as obtaining and analyzing adequate data). Students will present their research projects. Based on the course participants’ and the instructor’s feedback, students can update and refine their research projects.

    Learning outcomes:

    • Development of own marketing research project
    • presentation of own marketing research project
    • providing feedback on marketing research projects

    This course aims at preparing students to formulate their own marketing research problems (e.g., as parts of their dissertation projects), to shape their contribution with respect to the existing literature, and to identify the necessary data and methods to conduct their research projects. As benchmark for the students’ research projects, the actual standards with respect to innovative­ness, relevance, and rigor of the leading international marketing journals will be applied. Furthermore, implications for practice have to be considered.

    Form of assessment: Essay (60%), presentation (40%)

    Schedule
    Lecture
    03.03.21 Wednesday 09:00 – 12:00
    06.05.21 Thursday 09:00 – 12:00
    07.05.21 Friday 09:00 – 12:00
    MKT 910: Area Seminar Marketing
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: MKT 910
    Course Content

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

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

    Form of assessment: Oral participation.


    Seminar Dates are announced here.

    IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: IntRes
    Credits: 5
    Course Content

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll.

    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. Mini Research Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to the other participants. You will give a presentation on a current working paper or research project of yours and you will discuss a paper/presentation from one of your fellow students from another field (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in two weeks after the presentation.

     

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in the ‘Mini Research Day’. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant 10 days before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first serve basis.


    Course dates:

    February 15th, 2021 – Course Registration Deadline

    March 11th, 2021, 12:30pm-3:30pm, Kick-Off Meeting         

    April 12th, 2021 – Deadline to send paper to discussant (and in cc: to gess@uni-    mannheim.de)

    April 22th, 2021 – Mini Research Day (whole day symposium)

    April 28 th, 2021 – time TBA – Science Speed Dating Event

    May 28th, 2021, Presentation of research proposal (half- to full day symposium)

    June 13th, 2021 – Deadline to hand in interdisciplinary research proposal (to:gess uni-mannheim.de)

    MET: Interpretability and Fairness in Machine Learning
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Prior experience with R is helpful, but not necessary. Optional learning resources can be found here: https://rstudio.cloud/learn/primers

    Course Content

    Machine Learning (ML) is increasingly used to guide high-stakes decisions in various contexts such as college admissions, granting loans or hiring employees. By eliminating human judgment, ML-based decision-making promises to be neutral and objective and to find the right decisions in shorter time. At the same time, however, concerns are raised that algorithmic decision-making may foster discrimination and amplify existing biases that are fed into the models. This course discusses recent advances in the field of Interpretable and Fair ML: How can we explain predictions of black-box models? How can we measure and mitigate biases to make ML models fair? In addition to covering fairness and interpretability, the course will include a general introduction to supervised machine learning. Hands-on lab sessions will demonstrate how to train and interpret ML models using R.

    Course requirements & assessment:

    Presentation and  term paper (graded)

    Schedule
    Seminar
    1st group 11.03.21 – 17.06.21 Thursday 13:45 – 15:15 Sowi Zoom 03 Link
    2nd group 16.04.21 – 28.05.21 Friday 12:00 – 15:15 Sowi Zoom 04 Link
    MKT 902: Advances in Marketing Research
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MKT 902
    Credits: 6
    Course Content

    The primary goal of Advances in Marketing Research is to help students prepare to conduct research which is publishable in the leading research journals in their respective disciplines.  Hence, the feedback students receive will be consistent with that dispensed by the reviewers and editors of the most prestigious research journals in business (i.e., highly critical).  Even when a manuscript is accepted for publication at a leading journal, the authors typically receive mostly negative comments on their work.  It is important that students not take criticism of their research personally.  To do so would be extremely ego deflating and would interfere with their subsequent performance on other assignments.  Moreover, students need to develop the ability to accept and use criticism to be able to survive in the academic publishing world.

    Learning outcomes: Advances in Marketing Research is designed to assist doctoral candidates in acquiring a deeper understanding of the research process and a knowledge of the research tools which they will need to design and execute scientific research on behavioral and organizational issues in marketing.  An effort is made to help the students develop research judgment as well as research skills so that they will be better able to assess when a proposed piece of research is likely to be fruitful and when it is not.

    Form of assessment: Essay: 50%, presentation: 30%, discussion and simulation/statistical analysis: 20%

    Schedule
    Lecture
    05.03.21 – 18.06.21 Friday 15:30 – 17:00 online
  • Operations Management

    IS / OPM 910: Area Seminar Information Systems and Operations Management
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: IS / OPM 910
    Course Content

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

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

    Form of assessment: Oral participation.


    Seminar Dates are announced here.


    OPM 802: Dynamic and Stochastic Models in Supply Chain Research
    8 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: OPM 802
    Credits: 8
    Prerequisites

    Recommended: Fundamentals in mathematics and statistics

    Course Content

    The course introduces some fundamental techniques for stochastic modelling and optimization, and it discusses their application in supply chain research. Key topics include:

    • Stochastic processes
    • Markov chains
    • Stochastic dynamic programming
    • Inventory theory
    • Revenue management

    The course is taught in a seminar-style format.

    Learning outcomes: The course aims to introduce the participants to fundamental stochastic modeling techniques. Upon completion of this course, participants should be able (i) to read and understand corresponding academic papers and (ii) to develop and analyze stochastic models for supply chain management issues.

    Form of assessment: Term paper 30%, presentation 70%

    Schedule
    Lecture
    02.03.21 – 15.06.21 Tuesday 15:30 – 17:00 online
    OPM 806: Empirical Research in Operations Management
    8 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: OPM 806
    Credits: 8
    Prerequisites

    Recommended: Fundamentals of statistics

    Course Content

    A large part of research in operations management focusses on modeling and solving practical problems. In contrast to this “OR approach”, the objective of empirical research is to collect data about practical phenomena in order to describe, explain, or predict how those phenomena work. This module provides an overview of (mainly quantitative) empirical research approaches to investigate research questions in operations management and related fields. The focus in not on the comprehensive treatment of empirical research methods, but on how to proceed from having a basic research question to an appropriate research design and methodology. Hence, special emphasis will be placed on the importance of understanding the contingent relationship between the nature of the research question and the research design used to answer it. Topics covered include quantitative vs. qualitative empirical research, framing of research questions, engaging theory and grounding of hypotheses, measurement and operationalization, sampling, model specification, and mainstream research designs and methodologies. This will enable students to critically evaluate the quality of the majority of empirical research in operations management and to design convincing research of their own.

    The course will be taught using an interactive seminar style and is based on the discussion of a selection of papers.

    Learning outcomes: At the end of this course, students have gained the competence to initiate, design, implement, and evaluate empirical research in the social sciences as applied to operations management.

    Form of assessment: Oral exam (30 minutes) 60%, presentation 40%

    Schedule
    Lecture
    30.04.21 Friday 08:30 – 13:30 online
    07.05.21 Friday 08:30 – 13:30 online
    21.05.21 Friday 08:30 – 13:30 online
    28.05.21 Friday 08:30 – 13:30 online
    OPM 999: Project Study Operations
    9 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: OPM 999
    Credits: 9
    Course Content

    This course revolves around a research-oriented project that is carried out by the participants on an individual basis. Supported by their supervisors, participants will gain further experience in conducting research and will practice for the preparation of research proposals and own working papers. Topic and research question(s), project structure, and methodology are chosen independently by each student.

    Learning outcomes: The main intended learning outcome is to learn and apply the competences for conducting high-quality research studies in operations management. In addition, participants will practice their skills in how to present research findings.

    Form of assessment: Essay (70%), presentation (30%)

    IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: IntRes
    Credits: 5
    Course Content

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll.

    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. Mini Research Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to the other participants. You will give a presentation on a current working paper or research project of yours and you will discuss a paper/presentation from one of your fellow students from another field (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in two weeks after the presentation.

     

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in the ‘Mini Research Day’. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant 10 days before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first serve basis.


    Course dates:

    February 15th, 2021 – Course Registration Deadline

    March 11th, 2021, 12:30pm-3:30pm, Kick-Off Meeting         

    April 12th, 2021 – Deadline to send paper to discussant (and in cc: to gess@uni-    mannheim.de)

    April 22th, 2021 – Mini Research Day (whole day symposium)

    April 28 th, 2021 – time TBA – Science Speed Dating Event

    May 28th, 2021, Presentation of research proposal (half- to full day symposium)

    June 13th, 2021 – Deadline to hand in interdisciplinary research proposal (to:gess uni-mannheim.de)

    MET: Interpretability and Fairness in Machine Learning
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Prior experience with R is helpful, but not necessary. Optional learning resources can be found here: https://rstudio.cloud/learn/primers

    Course Content

    Machine Learning (ML) is increasingly used to guide high-stakes decisions in various contexts such as college admissions, granting loans or hiring employees. By eliminating human judgment, ML-based decision-making promises to be neutral and objective and to find the right decisions in shorter time. At the same time, however, concerns are raised that algorithmic decision-making may foster discrimination and amplify existing biases that are fed into the models. This course discusses recent advances in the field of Interpretable and Fair ML: How can we explain predictions of black-box models? How can we measure and mitigate biases to make ML models fair? In addition to covering fairness and interpretability, the course will include a general introduction to supervised machine learning. Hands-on lab sessions will demonstrate how to train and interpret ML models using R.

    Course requirements & assessment:

    Presentation and  term paper (graded)

    Schedule
    Seminar
    1st group 11.03.21 – 17.06.21 Thursday 13:45 – 15:15 Sowi Zoom 03 Link
    2nd group 16.04.21 – 28.05.21 Friday 12:00 – 15:15 Sowi Zoom 04 Link
  • Taxation

    ACC / TAX 910: Area Seminar Accounting and Taxation
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: ACC / TAX 910
    Course Content

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

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

    Form of assessment: Oral participation.


    Seminar Dates are announced here.

    ACC / TAX 920: Brown Bag Seminar
    ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: ACC / TAX 920
    Course Content

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

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

    Form of assessment: Class Participation


    Coursedates will be announced via email to registered participants.

    TAX 801: Business Taxation
    8 ECTS
    Lecturer(s)

    Course Type: core course
    Course Number: TAX 801
    Credits: 8
    Course Content

    This course integrates tax law with national and international tax planning. The main topics include:

    1. Fundamentals of tax planning and tax neutrality
    2. The choice of the organizational Form
    3. Flat tax and dual income Tax
    4. International tax planning
    5. Effective tax rates

    Learning outcomes: The course gives guidance to students who are interested in the impact of taxes on the decisions of firms. The focus is on investment and financing decisions as well as on location decisions both from a national and from an international perspective.

    Form of assessment: Presentation 50%, class participation 50%

    Schedule
    Lecture
    03.03.21 – 16.06.21 Wednesday 08:30 – 11:45 O 142 + online
    IntRes: Interdisciplinary Research in the Economic and Social Sciences (Bridge Course)
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: IntRes
    Credits: 5
    Course Content

    This course is exclusively geared towards students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'. Maximum number of participants is 15. If the course is not fully booked, non-GESS students from Business, Economics, or the Social Sciences or from other related disciplines can enroll.

    Course Content

    This course will introduce student to interdisciplinary research and aims at initiating projects of an interdisciplinary nature, thereby fostering the interdisciplinary spirit of the graduate students at the GESS. This year, the course will be given by one senior researchers from each center of the GESS, i.e., you will have the unique opportunity to receive truly interdisciplinary feedback on your work from three different angles.  

    The course consists of four core building blocks:

    1. Kick-Off & Introductory Session: What is interdisciplinary research.

    After a short introduction on the nature and success of interdisciplinary research as well as the structure of the course by the instructors, each participant will shortly (max 5 min, 2–3 slides per person) present the core idea of an interdisciplinary paper published in a top journal in her field. Please browse the recent issues of the most important journals in your field to find such a paper. Note that interdisciplinarity can have various aspects in this context (e.g., methods developed for a specific purpose in one field being used in another context, using a theoretical framework from one area to better understand a research question in another, using data generated in another context for a research project, ...). Your presentation should make clear, what the interdisciplinary innovation of the paper is.

    2. Mini Research Day

    The second component of the course is a ‘Mini-Research-Day’ which is intended to introduce the kind of topics you are working on to the other participants. You will give a presentation on a current working paper or research project of yours and you will discuss a paper/presentation from one of your fellow students from another field (10 min presentation, 5 min discussion, 10 min Q&A).

    3. Science Speed Dating

    The science speed dating event – organized by your student representatives – involves short bilateral talks between participants with the later possibility to match research interests. All course participants will participate in the speed dating event and are asked to develop at least one collaborative research proposal with a student from another field (preferably from our course).

    4. Project Presentations & Writeups

    This proposal will be presented by groups of 2 (in exceptional cases 3) students in a final meeting about four weeks after the speed dating event. These teams will also prepare a write-up of their proposal (max. 5 pages, incl. References) explaining the intended contribution to the literature, the interdisciplinary aspects of the project and the proposed procedure how to implement the project to be handed in two weeks after the presentation.

     

    Learning Outcomes: Upon successful completion of this course, students will

    • have gotten in touch with a variety of disciplinary research methods and perspectives from different fields
    • critically evaluate the strengths and weaknesses of these research methods
    • identify and develop an interdisciplinary research proposal and communicate their ideas clearly in both, a presentation and in writing.
    • have practiced to present their work to a critical, interdisciplinary audience and to discuss other students work in a format closely resembling that of most academic conferences.

    Form of assessment:

    This is a Pass/Fail course. To successfully pass the course, each student has to:

    • Give a short paper presentation in the introductory session.
    • Present, discuss, and participate in the ‘Mini Research Day’. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant 10 days before the research day.
    • Participate at the science speed dating event.
    • Present your interdisciplinary research proposal (group of two students) and subsequently hand in a write-up
    • Full and active participation in all four building blocks is necessary to pass the course.
    • The best interdisciplinary proposal will win a price.

    Please register by the registration deadline given below, by sending a title and an abstract of the research project/topic you would like to present during the ‘Mini Research Day’ to registration gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

    Please note that the course is limited to a maximum of 15 participants, and seats will be allocated on a first come first serve basis.


    Course dates:

    February 15th, 2021 – Course Registration Deadline

    March 11th, 2021, 12:30pm-3:30pm, Kick-Off Meeting         

    April 12th, 2021 – Deadline to send paper to discussant (and in cc: to gess@uni-    mannheim.de)

    April 22th, 2021 – Mini Research Day (whole day symposium)

    April 28 th, 2021 – time TBA – Science Speed Dating Event

    May 28th, 2021, Presentation of research proposal (half- to full day symposium)

    June 13th, 2021 – Deadline to hand in interdisciplinary research proposal (to:gess uni-mannheim.de)

    MET: Interpretability and Fairness in Machine Learning
    6 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: MET
    Credits: 6
    Prerequisites

    Prior experience with R is helpful, but not necessary. Optional learning resources can be found here: https://rstudio.cloud/learn/primers

    Course Content

    Machine Learning (ML) is increasingly used to guide high-stakes decisions in various contexts such as college admissions, granting loans or hiring employees. By eliminating human judgment, ML-based decision-making promises to be neutral and objective and to find the right decisions in shorter time. At the same time, however, concerns are raised that algorithmic decision-making may foster discrimination and amplify existing biases that are fed into the models. This course discusses recent advances in the field of Interpretable and Fair ML: How can we explain predictions of black-box models? How can we measure and mitigate biases to make ML models fair? In addition to covering fairness and interpretability, the course will include a general introduction to supervised machine learning. Hands-on lab sessions will demonstrate how to train and interpret ML models using R.

    Course requirements & assessment:

    Presentation and  term paper (graded)

    Schedule
    Seminar
    1st group 11.03.21 – 17.06.21 Thursday 13:45 – 15:15 Sowi Zoom 03 Link
    2nd group 16.04.21 – 28.05.21 Friday 12:00 – 15:15 Sowi Zoom 04 Link
    TAX 913: Empirical Taxation Research
    10 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: TAX 913
    Credits: 10
    Course Content

    Class sessions are mostly organized along the methods in the standard tool kit of empirical research. We start off each topic with a brief and easy overview of the method. Afterwards, a student will summarize a paper using the respective method and we will discuss in class. For each method, we identify a set of core papers which use the respective method, present examples of a state-of-the art application and are relevant topic wise. These core papers are summarized and discussed in class. We expect all students to read the core papers that we cover in class.

    In addition, we will talk about the research profession and “How to survive in academia”. Toward the end of the term, students present their summer project. Summer projects are conducted over the summer and presented during the beginning of the subsequent fall term.

    Learning outcomes:

    Overview of the most important topics and methods for causal identification in empirical tax research. Familiarize with state-of-the-art literature. We selected papers to be studied in class which (hopefully) cover the most important topics and methods.

    Developing of a research project and carrying out all phases of the projects, i.e., from identifying a research question to writing up a first draft. The class will guide you through all phases of the project. If this project turns out to be feasible and promising, it could well be a first dissertation paper. The projects are conducted over the summer.

    Insights on “How to survive in academia”: career paths, how to publish, how to write a referee report, how to be a good “academic citizen”, conferences, role of networking, etc..

    Form of assessment: Essay and/or presentation


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

    Schedule
    Lecture
    02.03.21 – 15.06.21 Tuesday 08:30 – 11:45 online
    05.03.21 – 18.06.21 Friday 15:30 – 17:00 online
    TAX 922: Reading Course Taxation Research
    5 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: TAX 922
    Credits: 5
    Course Content

    The reading course is aimed at 2nd and higher year Ph.D. students to support them during their research phase.


    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.

    Learning outcomes:

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

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

    Schedule
    Lecture
    01.03.21 – 14.06.21 Monday 10:15 – 11:45 online
    TAX 924: Applied Econometrics Seminar I
    4 ECTS
    Lecturer(s)

    Course Type: elective course
    Course Number: TAX 924
    Credits: 4
    Prerequisites

    Recommended: Applied Econometrics I

    Course Content

    This course provides the opportunity to learn about recent developments in empirical methodology and more generally any method which is not covered yet in the Applied Econometrics I module. It is also a platform to get feedback on methodological challenges in research projects. Students learn to identify relevant topics and the corresponding literature. They develop the code for self-instructing illustrative examples and critically present the empirical method in class.

    Topics of interest are chosen at the start of the semester and may include, for example, limited dependent variable regressions (binary, multinomial, sample selection, count data), matching estimators, quantile regressions, machine learning methods, event studies, regression discontinuity designs, or bunching estimators.

     

    Learning outcomes:

    • A sound understanding of advanced empirical research methods and their underlying assumptions
    • Students will be able to choose appropriate methods for given empirical problems

     

    Form of assessment: Presentations (50%), Participation (50%)

    Schedule
    Lecture
    03.03.21 – 16.06.21 Wednesday 17:15 – 18:45 O 151 and online