Comparer des méthodes

	Test de Chow pour la rupture structurelle	Régression linéaire multiple	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Économétrie	Statistique	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	1960	1886	2019
Auteur d'origine	Gregory C. Chow	Francis Galton; formalized by Karl Pearson	Wooldridge (textbook treatment); classical least squares
Type	Test for structural break in regression coefficients	Parametric linear model	Linear regression
Source fondatrice	Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	Chow breakpoint test, structural break test, Chow yapısal kırılma testi	MLR, OLS regression, multiple regression, linear regression with multiple predictors	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	2	8	5
Résumé	The Chow test, introduced by Gregory Chow in 1960, checks whether the coefficients of a linear regression are the same across two subsamples — that is, whether a structural break occurs at a known point such as a policy change, crisis, or regime shift. It compares the fit of a single pooled regression with the combined fit of two separate regressions; a large improvement from splitting indicates the relationship differs between the two periods or groups.	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.	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).