Comparer des méthodes

	Modèle ARMA à paramètres variant dans le temps (TVP-ARMA)	Modèle d'espace d'états (Filtre de Kalman)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1976	1990
Auteur d'origine	Cooley & Prescott (1976); further formalised by Harvey (1989)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Type	State-space time series model	State space time series model
Source fondatrice	Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Alias	TVP-ARMA, time-varying ARMA, state-space ARMA, locally stationary ARMA	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Apparentées	3	4
Résumé	The time-varying parameter ARMA (TVP-ARMA) model extends the classical ARMA framework by allowing the autoregressive and moving-average coefficients to evolve over time. Embedded in a state-space representation and estimated via the Kalman filter, it captures structural change and parameter instability in time series without requiring an explicit breakpoint.	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.