• As the programming part of the course starts with an introduction to Python, it is not required to have previous knowledge in Python. At the same time, programming experience will be helpful for successfully completing the course.
• As the methods covered in this course can be applied to many different settings, the course explicitly targets students/researchers from different areas, including accounting, economics, finance, marketing, and management.
• Note that the course focusses on quantitative approaches. Thus, it might not be the best fit for students who exclusively work qualitatively with text data.

The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing textual data, for example large scale news or social media datasets, using techniques and methods from the domain of natural language processing. The students will present their results and write a paper about their research.

This reading course provides a hands-on and paper-based approach to understanding and analyzing data. For many projects, collection of new data or experimental designs are the only way to answer a research question or to provide the decisive complementary evidence. Different ways to collect data can have important implications for model estimation and evaluation, parameter inference, and policy conclusions. Standard econometric methods start from assumptions about the sampling procedure and try to cope with the limitations of a given dataset. Instead, we start at the design stage and examine the interplay between sampling and experimental methods, statistical inference and estimation of causal effects. We will use the German Business Panel as point in case and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes. In each session, one of the participants will present a research paper, which we will discuss in light of concrete implementation at trial scale. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic.

In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to use the newly collected data or learn about experimental results.

This course is aimed at doctoral students at GESS. The seminar hosts speakers from academia and industry to discuss latest advances and challenges associated with the transition towards a decarbonized energy economy. Topics covered include the economics and management of sustainability activities and clean energy technologies across all sectors of the economy with a particular focus on the energy sector, transportation services, and carbon-free manufacturing processes.

Course participants need to attend the seminar talks and the corresponding preparation sessions. In the preparation sessions, students are asked to present a paper and take the role of a discussant. Readings may additionally include recent theory or empirical papers.

Learning outcomes: The primary objective of the course is to introduce students to current research paradigms on the covered topics and to identify promising avenues for future research. Moreover, students receive a training on how to present and evaluate papers in seminars and conferences.

Form of assessment: Participation (20%), Paper presentations and discussions (80%)

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

The meetings discuss recent advances in analytical accounting, tax, or organizations research. The focus of the discussion is the academic rigor of the studies, the relevance of the topic, and the writing style of the authors to learn more about the means of getting academic papers published in top peer-reviewed journals.

Every participant must serve as a moderator at least once. Active participation in the discussions of all other sessions is expected. In addition, the participants are asked to provide a written report in the style of an academic journal review for one paper that they did not moderate. For this purpose, a preparation session and feedback session for the moderation and the written report is additionally required.

Form of assessment: Participation (25%), Paper moderation (25%), and written assignment (50%)

Responsible teacher: Dr. Sebastian Kronenberger

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

The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing textual data, for example large scale news or social media datasets, using techniques and methods from the domain of natural language processing. The students will present their results and write a paper about their research.

This reading course provides a hands-on and paper-based approach to understanding and analyzing data. For many projects, collection of new data or experimental designs are the only way to answer a research question or to provide the decisive complementary evidence. Different ways to collect data can have important implications for model estimation and evaluation, parameter inference, and policy conclusions. Standard econometric methods start from assumptions about the sampling procedure and try to cope with the limitations of a given dataset. Instead, we start at the design stage and examine the interplay between sampling and experimental methods, statistical inference and estimation of causal effects. We will use the German Business Panel as point in case and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes. In each session, one of the participants will present a research paper, which we will discuss in light of concrete implementation at trial scale. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic.

In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to use the newly collected data or learn about experimental results.

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

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

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

The reading course is particularly aimed at 2nd and higher year Ph.D. students to support them during their research phase. 1st year PhD students are welcomed to attend the class as well. Students can attend and earn credits for both this class as well as the related class TAX 923 (which is taught in the fall semester).

• As the programming part of the course starts with an introduction to Python, it is not required to have previous knowledge in Python. At the same time, programming experience will be helpful for successfully completing the course.
• As the methods covered in this course can be applied to many different settings, the course explicitly targets students/researchers from different areas, including accounting, economics, finance, marketing, and management.
• Note that the course focusses on quantitative approaches. Thus, it might not be the best fit for students who exclusively work qualitatively with text data.

This reading course provides a hands-on and paper-based approach to understanding and analyzing data. For many projects, collection of new data or experimental designs are the only way to answer a research question or to provide the decisive complementary evidence. Different ways to collect data can have important implications for model estimation and evaluation, parameter inference, and policy conclusions. Standard econometric methods start from assumptions about the sampling procedure and try to cope with the limitations of a given dataset. Instead, we start at the design stage and examine the interplay between sampling and experimental methods, statistical inference and estimation of causal effects. We will use the German Business Panel as point in case and implement cutting-edge methods to gain insights into the causal mechanisms behind reported outcomes. In each session, one of the participants will present a research paper, which we will discuss in light of concrete implementation at trial scale. Participants are encouraged to present research that is valuable for their own thesis or may be assigned to present a topic.

In addition to presenting a paper and participating in the discussion, students are expected to write a short technical report that summarizes the methods and implications in a way useful for peers who want to use the newly collected data or learn about experimental results.

The goal of this lab exercises is to guide students through the typical steps of a scientific data-science project from problem formulation to data acquisition, selection of methods, analysis and presentation / documentation. The focus of this lab will be on analyzing textual data, for example large scale news or social media datasets, using techniques and methods from the domain of natural language processing. The students will present their results and write a paper about their research.