Comparer des méthodes

	Modèle autorégressif à paramètres variant dans le temps (TVP-AR)	Modèle d'espace d'états (Filtre de Kalman)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1976–2005	1990
Auteur d'origine	Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Type	Time-series model with drifting coefficients	State space time series model
Source fondatrice	Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Alias	TVP-AR, time-varying AR, state-space AR with drifting coefficients, random-walk coefficient AR	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Apparentées	4	4
Résumé	The Time-Varying Parameter Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fixed break date.	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.