เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Spatial Panel Event Study× | การศึกษาเหตุการณ์แบบแผง (Panel Event Study)× | |
|---|---|---|
| สาขาวิชา | การอนุมานเชิงสาเหตุ | การอนุมานเชิงสาเหตุ |
| ตระกูล | Regression model | Regression model |
| ปีกำเนิด≠ | 2010s–2020s | 1990s–2020s (modern panel formulation) |
| ผู้ริเริ่ม≠ | Synthesized from spatial econometrics and panel event-study literatures; formalized in applied work in the 2010s–2020s | Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation |
| ประเภท≠ | Quasi-experimental causal inference | Quasi-experimental / causal panel design |
| แหล่งต้นตำรับ≠ | Sun, L., & Callaway, B. (2021). Difference-in-differences estimators of intertemporal treatment effects. arXiv:2109.10157. link ↗ | Freyaldenhoven, S., Hansen, C., Perez-Orive, J., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper 29170. National Bureau of Economic Research. link ↗ |
| ชื่อเรียกอื่น | spatial event study, spatial DiD event study, geo-panel event study, spatial panel ES | event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model |
| ที่เกี่ยวข้อง | 4 | 4 |
| สรุป≠ | Spatial panel event study extends the classical panel event-study design to settings where units are geographically located and outcomes may spill over across space. By combining event-time indicators with spatial weights matrices, it estimates dynamic treatment effects while explicitly accounting for spatial autocorrelation, geographic spillovers, and cross-unit contamination that would bias conventional event studies. | A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged from untreated units at each point in calendar time relative to their treatment date, making both pre-treatment trend violations and post-treatment effect trajectories immediately visible. |
| ScholarGateชุดข้อมูล ↗ |
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