Advanced Empirical Finance and Accounting

Course Description

The analysis of financial market data is an important requisite to identify market efficiencies and to evaluate (regulatory) measure taken to overcome any malfunctioning. Digitization and the associated technological breakthroughs offer a unique opportunity to collect and analyze financial and accounting data.

The applied empirical course will equip students with tools to collect and examine financial data. The course will start with some regression fundamentals. In particular, it will cover the classical linear regression model and will discuss its limitations. Building on that, the course will introduce the state-of-the-art techniques to overcome problems of classical linear regression model. These tools will include panel estimation methodologies as well as instrumental variables estimation. In the course of the entire lecture, current research published in top academic journals will be used as examples.

Learning Objectives

Upon successful completion of the course, students will be able to:

  1. understand methodologies to get causal inference,
  2. apply the proper tools on various question at hand to carry out own empirical research in the field of finance and accounting,
  3. collect and work with financial market data.


Course Requirements

  • Basic Statistics and Econometrics
  • Basic knowledge of finance and accounting theory is useful (not necessarily required)

Course Facts

  • Format: Lecture and Tutorials (blocked)
  • Course of Studies: TBA
  • Language: English
  • Semester: Summer term 2021
  • Dates: 13.07., 14.07., 20.07., 21.07., 22.07.
  • Time: 10AM - 6PM (blocked classes with tutorials)
  • Please register for the course via the form on this page by July 9, 2021


  1. Introduction
    1. Some basic ideas
    2. Experiments
    3. Selection problem
  2. The classical linear regression model
    1. Regression fundamentals
    2. What may go wrong
  3. Instrument variable estimation
  4. Regression with panel data
    1. Fixed effects regression
    2. Differences-in-differences estimation
  5. Data collection



  • Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2009.
  • Cameron, A. Colin, and Pravin K. Trivedi. Microeconometrics: methods and applications. Cambridge university press, 2005.
  • Degryse, Hans, Moshe Kim, and Steven Ongena. Microeconometrics of banking: methods, applications, and results. Oxford University Press, USA, 2009.
  • Various (topical) academic research paper