2215 Data Management and Analysis in Accounting Research
Univ.Prof. Dr. Zoltán Novotny-Farkas
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
25.09.2019 bis 27.09.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Doktorat/PhD
Wochentag Datum Uhrzeit Raum
Montag 02.12.2019 15:00 - 17:30 D3.0.222
Freitag 06.12.2019 14:00 - 16:30 D3.3.274
Montag 09.12.2019 15:00 - 17:30 TC.-1.61
Mittwoch 11.12.2019 15:00 - 17:30 LC.-1.021 Übungsraum
Montag 16.12.2019 15:00 - 17:30 TC.-1.61
Freitag 20.12.2019 14:00 - 16:30 TC.-1.61
Mittwoch 08.01.2020 15:00 - 18:30 TC.-1.61
Montag 13.01.2020 15:00 - 17:30 D3.3.274

Inhalte der LV

This module is designed to introduce students to some of the core issues associated with empirical accounting research, basic research design issues, collecting and handling large datasets, and analysing data and tabulating results. The module is primarily based around a single published empirical study and will involve students replicating the main analyses reported therein and developing a research design that aims to extend the original analysis. The replication element of the module provides the basis for developing the fundamental data and programming skills required to undertake large sample empirical research. In addition, students will be required to develop and implement extensions of the original research design.

Lernergebnisse (Learning Outcomes)

Students will be able to handle large widely-used commercial databases (CRSP, Compustat, IBES). They will learn important data management and econometric skills using Stata (statistical software) and how to handle practical problems arising in repications. In particular, students will engage with fundamental issues in empirical accounting research (e.g., the distinction between announcement dates and fiscal year ends, determination of return intervals, the distinction between information content versus value relevance, matching data from different data sources, etc.).

Regelung zur Anwesenheit

Students must attend at least 6 out of the 9 sessions to pass the course.


The module will comprise a mixture of lectures, computer-based workshops and interactive student-led presentations.

Leistung(en) für eine Beurteilung

In-class participation: 20%

Completion of preparatory tasks: 30%

Term paper submitted at the end of the module: 50%

Zuletzt bearbeitet: 30.08.2019