M&S Research Hub is organizing a 3-day online workshop about "The Design of Event Studies" using R. The workshop setting is highly interactive and includes real-life data applications. The workshop content is designed to meet all levels of proficiency. Accordingly, no prior knowledge of event studies is needed. However, a basic understanding of R, statistical, and econometric methods is required.
This workshop provides an excellent opportunity for scholars, researchers, and data practitioners to learn and enhance their knowledge about the design and uses of Events studies. The event study is the most basic and oldest causal inference research design. It exists before experimentation. It existed before statistics. It most likely predates human language. It may have existed before humans. The premise is that an event occurred at a specific moment, causing a treatment. The effect of such treatment is whatever changed from before the occurrence to after.
The econometrics of event studies has various implications. It is designed across several domains, such as events studies with regression, events studies with multiple groups, and events studies with stock prices. Over three days, the workshop will cover an extended list of topics that includes the following:
Ground basics of Empirical Methods and
Introduction of Differences-in-Differences (Foundation of Event Studies):
- The starting point… Reality or Theory?
- Constructing Knowledge
- The starting point… Differences-in-Differences (DiD)
- Regression framework for DiD
- Assumptions of DiD
- Common Questions of DiD
- R Application
From Differences-in-Differences to Event Studies:
- Dynamic Differences-in-Differences
- Constructing the time dummy period variables for DiD
- Common Specifications of Dynamic Differences in Differences.
- Full event study specification.
- Interpretation of Event Study plots.
- R Practice: Simulation and Estimation Event Studies.
Introduction of Treatment Heterogeneity (New Event Studies):
-Heterogeneity in what exactly?
-Heterogeneity in time effects
-Heterogeneity in cross-sectional effects
-Heterogeneity in group effects due to differential absorption timming.
-Weakness of TWFE under heterogeneity
-Callaway & Sant’Anna estimator