Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robust Panel Event Study× | Dinamiskā "starpību starpībās" metode× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads | 2021 | 2021 |
| Autors≠ | Sun & Abraham (2021); Freyaldenhoven, Hansen, Shapiro & Weidner (2021) | Callaway & Sant'Anna; Sun & Abraham |
| Tips≠ | Quasi-experimental / causal inference | Causal inference / quasi-experimental |
| Pirmavots≠ | Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Citi nosaukumi | robust event-study estimator, heteroskedasticity-robust panel event study, staggered-robust event study, robust ES design | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | A robust panel event study extends the standard panel event study design by applying heteroskedasticity- and autocorrelation-robust (HAC) standard errors and, where staggered treatment adoption exists, interaction-weighted estimators that remain valid even when treatment effects are heterogeneous across cohorts and time periods. It is widely used in economics, finance, and policy research to trace the dynamic causal path of an intervention. | Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time. |
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