Comparer des méthodes

	Modèle d'espace d'états (Filtre de Kalman)	Modèle structurel de séries temporelles (Modèle structurel de base)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1990	1990
Auteur d'origine	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Andrew C. Harvey
Type	State space time series model	State-space (unobserved components) time series model
Source fondatrice	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
Alias	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
Apparentées	4	4
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.	The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.