Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utafiti wa Tukio la Paneli ya Anga× | Utafiti wa Tukio la Paneli× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2010s–2020s | 1990s–2020s (modern panel formulation) |
| Mwanzilishi≠ | 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 |
| Aina≠ | Quasi-experimental causal inference | Quasi-experimental / causal panel design |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | 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 |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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