Working Paper: CEPR ID: DP13477
Authors: Kurt Schmidheiny; Sebastian Siegloch
Abstract: We discuss important properties and pitfalls of panel-data event study designs. We derive three main results. First, binning of effect window endpoints is a practical necessity and key for identification of dynamic treatment effects. Second, event study designs with binned endpoints and distributed-lag models are numerically identical leading to the same parameter estimates after correct reparametrization. Third, classic dummy variable event study designs can be generalized to models that account for multiple events of different sign and intensity of the treatment, which are particularly interesting for research in labor economics and public finance. We show the practical relevance of our methodological points in a replication study.
Keywords: event study; distributed lag; applied microeconomics; credibility revolution
JEL Codes: C23; C51; H00; J08
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| binning of effect window endpoints (C22) | identification of dynamic treatment effects (C22) |
| binning of effect window endpoints (C22) | separation of dynamic effects from secular time trends (C22) |
| event study designs with binned endpoints (C41) | distributed lag models (C32) |
| classic dummy variable event study designs (C22) | generalization to models with multiple events (C30) |
| statistically significant dynamic effects (C22) | alignment with previous difference-in-difference estimates (C22) |