Võrdle meetodeid

	Ruumiiline erinevus-kahest-erinevusest	Ruumi viivis mudel (SAR / ruumiline autoregressiivne)
Valdkond	Põhjuslik järeldamine	Ruumianalüüs
Perekond	Regression model	Regression model
Tekkeaasta	2015	1988
Looja	Delgado & Florax	Anselin (textbook formalisation); LeSage & Pace
Tüüp	Quasi-experimental estimator	Spatial autoregressive regression
Algallikas	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.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Rööpnimetused	Spatial DiD, Geo-DiD, Difference-in-Differences with Spatial Autocorrelation, Mekansal Fark-içinde-Farklar	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Seotud	3	5
Kokkuvõte	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.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.