Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Ruimtelijke Onderbroken Tijdreeks× | Ruimtelijke Difference-in-Differences× | |
|---|---|---|
| Vakgebied | Causale inferentie | Causale inferentie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1990s–2000s | 2015 |
| Grondlegger≠ | Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s | Delgado & Florax |
| Type≠ | Quasi-experimental causal inference with spatial adjustment | Quasi-experimental estimator |
| Oorspronkelijke bron≠ | McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 978-0803913950 | Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 126, 35–40. DOI ↗ |
| Aliassen | Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS | Spatial DiD, Geo-DiD, Difference-in-Differences with Spatial Autocorrelation, Mekansal Fark-içinde-Farklar |
| Verwant≠ | 6 | 3 |
| Samenvatting≠ | 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. | Spatial Difference-in-Differences (Spatial DiD) extends the classical DiD estimator to settings where observations are geo-referenced and outcomes may be spatially autocorrelated or subject to spillover effects. Introduced by Delgado and Florax (2015), the method augments the standard two-way fixed-effects DiD regression with a spatial lag or spatial error term, yielding unbiased treatment-effect estimates even when policy shocks propagate across geographic units. It is used by economists, regional scientists, and urban planners evaluating place-based interventions such as infrastructure investment, environmental regulations, or zoning reforms. |
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