Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Пространствен метод на синтетичния контрол× | Пространствени прекъснати времеви редове× | |
|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2003–2010s | 1990s–2000s |
| Създател≠ | Abadie & Gardeazabal (2003); extended to spatial settings by subsequent applied econometric work | Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s |
| Тип≠ | Quasi-experimental causal inference | Quasi-experimental causal inference with spatial adjustment |
| Основополагащ източник≠ | Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132. DOI ↗ | McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 978-0803913950 |
| Други названия | spatial SCM, geographic synthetic control, spatial SC, spatial counterfactual control | Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS |
| Свързани | 6 | 6 |
| Резюме≠ | The Spatial Synthetic Control Method adapts the classic synthetic control framework to settings where treated and donor units are defined by geographic location. By constructing a weighted combination of spatially proximate or comparable control regions, the method estimates what would have happened to a treated area absent the intervention, while explicitly accounting for geographic spillovers, spatial autocorrelation, and contiguity among units. | Spatial Interrupted Time Series (Spatial ITS) extends the classic ITS design to settings where units are geo-referenced and outcomes in one location may spill over into or correlate with outcomes in neighbouring locations. It estimates the causal effect of a discrete intervention on an outcome time series while explicitly modelling geographic autocorrelation, preventing biased standard errors and enabling detection of spatial spillovers. |
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