Comparer des méthodes

	Modèle d'espace d'états (Filtre de Kalman)	Autoregression vectorielle bayésienne (BVAR)	Modèle à changement de régime markovien (MS-AR / MS-VAR)	Modèle structurel de séries temporelles (Modèle structurel de base)
Domaine	Économétrie	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model	Regression model
Année d'origine	1990	1986	1989	1990
Auteur d'origine	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Hamilton (1989); Kim & Nelson (1999)	Andrew C. Harvey
Type	State space time series model	Bayesian multivariate time-series model	Regime-switching 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.	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.	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)	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM)
Apparentées	4	5	5	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.	Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.	The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.	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.