Comparer des méthodes

	MCO de Fourier (Moindres Carrés Ordinaires augmentés de termes de Fourier)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2004	2019
Auteur d'origine	Becker, Enders, and Hurn	Wooldridge (textbook treatment); classical least squares
Type	Augmented linear regression	Linear regression
Source fondatrice	Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899–906.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	Fourier OLS, Fourier-augmented OLS, trigonometric OLS, smooth structural break OLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	6	5
Résumé	Fourier OLS is an OLS regression extended by adding low-frequency trigonometric (sine and cosine) terms to the regressor matrix. These Fourier components approximate smooth, gradual structural changes in the regression relationship over time without requiring knowledge of the number, timing, or form of the breaks.	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).