Сравнение методов

	Spatial Regression	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1988	2019
Автор метода	Luc Anselin	Wooldridge (textbook treatment); classical least squares
Тип	Spatial regression (cross-sectional)	Linear regression
Основополагающий источник	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
Другие названия	spatial econometrics, spatial lag model, spatial error model, SAR / SEM	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Связанные	5	5
Сводка	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).