Methoden vergelijken

	Global Spatial Error Model	Global Spatial Durbin Model (SDM)
Vakgebied	Ruimtelijke analyse	Ruimtelijke analyse
Familie	Regression model	Regression model
Jaar van ontstaan	1988	2009
Grondlegger	Luc Anselin	Durbin (1960); adapted to spatial context by LeSage & Pace (2009)
Type	Spatial regression model	Spatial regression model
Oorspronkelijke bron	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247
Aliassen	SEM, spatial error model, spatial error regression, global SEM	SDM, Spatial Durbin Model, global SDM, spatially lagged X model with spatial lag
Verwant	5	5
Samenvatting	The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations.	The Global Spatial Durbin Model extends the spatial lag model by including not only a spatially lagged dependent variable but also spatially lagged independent variables (WX). A single set of global coefficients applies uniformly across all locations, making it suitable for estimating average spillover effects when spatial dependence is pervasive throughout the study region.