Comparer des méthodes

	Modèle d'espace d'états (Filtre de Kalman)	Moindres Carrés Pondérés (MCP)
Domaine	Économétrie	Statistique
Famille	Regression model	Regression model
Année d'origine	1990	1935
Auteur d'origine	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Alexander Craig Aitken
Type	State space time series model	Weighted linear estimator
Source fondatrice	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Alias	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Apparentées	4	3
Résumé	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.