Comparer des méthodes

	Moindres Carrés Ordinaires à Paramètres Variables dans le Temps (MCO-PVT)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1976	2019
Auteur d'origine	Cooley & Prescott (1976); further developed by Harvey (1990)	Wooldridge (textbook treatment); classical least squares
Type	Time-series regression with evolving coefficients	Linear regression
Source fondatrice	Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	TVP-OLS, time-varying coefficient regression, rolling OLS, locally weighted OLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	4	5
Résumé	Time-Varying Parameter OLS extends classical ordinary least squares to allow regression coefficients to change over time. Instead of assuming fixed slopes throughout the sample, the model treats each coefficient as a stochastic process, tracking how economic relationships evolve — making it well-suited for analysing structural change in time-series data.	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).