Comparer des méthodes

	Modèle d'espace d'états (Filtre de Kalman)	Modèle à changement de régime markovien (MS-AR / MS-VAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1990	1989
Auteur d'origine	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Hamilton (1989); Kim & Nelson (1999)
Type	State space time series model	Regime-switching time series model
Source fondatrice	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Alias	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR
Apparentées	4	5
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 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.