השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מחקר אירוע פאנל בייסיאני× | הפרש-הפרשים דינמי× | |
|---|---|---|
| תחום | הסקה סיבתית | הסקה סיבתית |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 2010s–2020s | 2021 |
| הוגה השיטה≠ | Developed from panel event-study literature (Sun & Abraham 2021; Freyaldenhoven et al. 2021) combined with Bayesian estimation frameworks | Callaway & Sant'Anna; Sun & Abraham |
| סוג≠ | Bayesian causal panel estimator | Causal inference / quasi-experimental |
| מקור מכונן≠ | Freyaldenhoven, S., Hansen, C., Shapiro, J. M., & Teso, E. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper No. 29170. National Bureau of Economic Research. link ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| כינויים | Bayesian event-study estimator, Bayesian dynamic DiD, Bayesian panel ES, Bayes event study | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| קשורות | 4 | 4 |
| תקציר≠ | Bayesian Panel Event Study is a causal inference design that estimates dynamic treatment effects around a datable event using panel data, replacing classical frequentist estimation with Bayesian posterior inference. It produces period-by-period effect estimates with full probability distributions, enabling principled uncertainty quantification, regularization of noisy pre-trend coefficients, and probabilistic tests of parallel trends. | 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. |
| ScholarGateמערך נתונים ↗ |
|
|