השוואת שיטות

	רגרסיה מרחבית (מודלי השתרעות מרחבית ושגיאה מרחבית)	רגרסיית ריבועים פחותים רגילים (OLS)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1988	2019
הוגה השיטה	Luc Anselin	Wooldridge (textbook treatment); classical least squares
סוג	Spatial regression (cross-sectional)	Linear regression
מקור מכונן	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
כינויים	spatial econometrics, spatial lag model, spatial error model, SAR / SEM	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
קשורות	5	5
תקציר	Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model, where the dependence sits in the error term.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).