GGS-Workshop "Empirical Methods and Applications in Archival Data Research"
- https://www.uni-giessen.de/de/fbz/zentren/ggs/veranstaltungen/index_html/wise2026/archival-data-research
- GGS-Workshop "Empirical Methods and Applications in Archival Data Research"
- 2027-02-17T09:00:00+01:00
- 2027-02-19T17:00:00+01:00
17.02.2027 09:00 bis 19.02.2027 17:00 (Europe/Berlin / UTC100)
Room 001, Licher Strasse 68, 35394 Giessen
| Instructor: | Professor Dr Peter Limbach | |
| Dates: | February 17–19, 2027, 9.00 am to 5.00 pm respectively | |
| Max. participants: | 10 | |
| Course language: | English and/or German | |
| Registration Deadline: | January 18, 2027 | |
| ECTS: | 4 |
Objectives
Scholars conducting empirical research must understand not only econometric methods but also the strengths, assumptions, and limitations of empirical research designs. This is particularly important for archival research, which relies on data that exist prior to the study (e.g., publicly available firm or household data or textual data). In this course, participants will develop a deeper understanding of the main econometric methods used in empirical research and learn how to critically evaluate and apply these methods. Particular emphasis is placed on sound empirical research design (e.g., sample construction, the use of variables to operationalize theoretical constructs), as well as on endogeneity, standard estimation techniques (e.g., fixed effects and robust standard errors), and identification strategies (e.g., difference-in-differences and instrumental variables). Throughout the course, participants will critically discuss leading research papers that employ these methods and learn how to (not to) exploit natural experiments for causal identification. They will also become familiar with commonly used research databases, their advantages and limitations, and practical issues related to data collection and preparation. A STATA manual will be provided to support the empirical exercises.
You can find more details in the syllabus "Empirical Methods and Applications in Archival Data Research".