Comparer des méthodes

	Régression apparemment non liée (SUR)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1962	2019
Auteur d'origine	Arnold Zellner	Wooldridge (textbook treatment); classical least squares
Type	System regression (multi-equation)	Linear regression
Source fondatrice	Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	SUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	5
Résumé	Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS.	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).