Comparer des méthodes

	Modèle structurel de séries temporelles (Modèle structurel de base)	Modèle de Vector Autoregression (VAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1990	2005
Auteur d'origine	Andrew C. Harvey	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Type	State-space (unobserved components) time series model	Multivariate time-series model
Source fondatrice	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Apparentées	4	4
Résumé	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.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).