विधियों की तुलना करें

	ग्लोबल स्पैशियल एरर मॉडल (SEM)	ग्लोबल स्पैशियल डर्बिन मॉडल (SDM)
क्षेत्र	स्थानिक विश्लेषण	स्थानिक विश्लेषण
परिवार	Regression model	Regression model
उद्भव वर्ष	1988	2009
प्रवर्तक	Luc Anselin	Durbin (1960); adapted to spatial context by LeSage & Pace (2009)
प्रकार	Spatial regression model	Spatial regression model
मौलिक स्रोत	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
उपनाम	SEM, spatial error model, spatial error regression, global SEM	SDM, Spatial Durbin Model, global SDM, spatially lagged X model with spatial lag
संबंधित	5	5
सारांश	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.