Comparer des méthodes

	Modèle d'espace d'états (Filtre de Kalman)	Modèle de Vector Autoregression (VAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1990	2005
Auteur d'origine	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Type	State space 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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
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.	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).