Sammenlign metoder

	SAC-modellen (Spatial Autoregressive Combined)	Spatial Lag Model (SAR / Spatial Autoregressive)
Fagområde	Rumlig analyse	Rumlig analyse
Familie	Regression model	Regression model
Oprindelsesår	2009	1988
Ophavsperson	James LeSage & R. Kelley Pace	Anselin (textbook formalisation); LeSage & Pace
Type	Combined spatial dependence regression model	Spatial autoregressive regression
Oprindelig kilde	LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. ISBN: 978-1-4200-6424-7	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Aliasser	SARAR Model, Spatial Autoregressive Model with Autoregressive Disturbances, Cliff-Ord Combined Model, Uzamsal Otoregresif Birleşik Model	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Relaterede	3	5
Resumé	The Spatial Autoregressive Combined (SAC) model, also known as the SARAR model, simultaneously accounts for spatial dependence in both the dependent variable and the error term. Formalized by LeSage and Pace (2009), the SAC model combines the spatial lag model and the spatial error model into a single framework, estimating two distinct spatial autoregressive parameters — one capturing substantive spatial interaction among outcomes and another capturing residual spatial correlation among disturbances.	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.