Applied Empirical Research: Methods and Practical Implementation

TAX 620 for Master students (Dieser Kurs wird nur in Englisch angeboten)

Lecturers Prof. Dr. Philipp Dörrenberg,
Prof. Dr. Johannes Voget
Frequency Fall semester
Courses Lecture and Exercises
Language English
Form of Assessment Term paper based on own research project and presentation in class
Richard Winter, M.Sc.



For further information please contact Richard Winter.

Course Details

Students gain practical experience in performing empirical work and are provided an overview of the most important methods and approaches for applied empirical research.

One part of the course focuses on hands-on empirical applications and students learn how to conduct their own empirical analysis. For this purpose, students are introduced to the usage of a statistical software package (“R”) and to the access and analysis of large data sets (in particular firm databases sets such as Amadeus or Compustat). Examples will will always be from research in the field of taxation. This part of the course is very practically oriented.

The other part of the course teaches the most important empirical methods and approaches to estimate causal effects. These include, but are not restricted to, randomized experiments, linear regression, fixed effects estimators, difference(s)-in-difference(s), instrumental variables and regression discontinuity design. The focus is on understanding the advantages and disadvantages of the available econometric methods and less on a highly technical presentation. Illustrative examples will always be from the field of taxation. This part of the course complements the empirical-application part of the course: the methods taught in this part are practically implemented in the other (applied-empirics) part of the course. The class features a guest lecture by a partner of a big-4 accounting firm.

Overall, the course equips students with skills on data handling, software, coding, and methodological aspects in the context of empirical work. These skills are very valuable for data-focused (big data) work both in industry and academia. The course is generally also suited for students without significant background or interest in taxation; examples will be from taxation, but the taught methods and empirical applications generalize beyond tax topics.

To receive a grade, students are required to conduct an independent empirical project using statistical software and real-world data (either an own research idea or a replication of an existing research paper). In addition, students write a short term-paper which presents the results from their empirical analysis and they are asked to present the results from their empirical project in class.

    Course Structure

  • Learning Outcomes

    • Overview of most important methods and approaches for applied empirical research.
    • Hands-on practice of empirical analysis using statistical software.
  • Table of Contents

    1. Causality
    2. Randomized Experiments
    3. Regression and Fixed Effects
    4. Differences in Differences
    5. Regression Discontinuity Design
    6. Current Topics in Taxation Research and Overview of Available Data
    7. Instrumental Variables
    8. Bunching
    9. How to Set Up a Research Project



    1. Do-files
    2. Read and Examine Data
    3. Create Sample for Analysis
    4. Simple Regression Analysis and Tests
    5. Graphs and Visualizing
    6. Programming (globals/locals/loops)
  • Prerequisites

    Formal: None

    Recommended: Introductory classes on statistics and/or econometrics at Bachelor level.