Syllabus

Title
2381 Data Management and Analysis in Accounting Research
Instructors
Univ.Prof. Dr. Zoltán Novotny-Farkas
Contact details
  • Type
    FS
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
10/04/21 to 10/08/21
Registration via LPIS
Notes to the course
Subject(s) Doctoral/PhD Programs
Dates
Day Date Time Room
Tuesday 11/23/21 09:00 AM - 12:00 PM LC.2.064 Raiffeisen PC Raum
Tuesday 11/30/21 09:00 AM - 12:00 PM LC.2.064 Raiffeisen PC Raum
Tuesday 12/07/21 09:00 AM - 12:00 PM LC.2.064 Raiffeisen PC Raum
Tuesday 12/14/21 09:00 AM - 12:00 PM LC.2.064 Raiffeisen PC Raum
Tuesday 12/21/21 09:00 AM - 12:00 PM LC.2.064 Raiffeisen PC Raum
Tuesday 01/11/22 09:00 AM - 12:00 PM LC.2.064 Raiffeisen PC Raum
Tuesday 01/18/22 09:00 AM - 12:00 PM LC.2.064 Raiffeisen PC Raum
Tuesday 01/25/22 09:00 AM - 11:30 AM D2.-1.019 Workstation-Raum

Contents

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.

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.).

Attendance requirements

Students must attend at least 5 out of the 8 sessions to pass the course.

Teaching/learning method(s)

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

Assessment

In-class participation: 20%

Completion of preparatory tasks: 30%

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

Last edited: 2021-04-16



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