Comparer des méthodes

	Modèle d'Équilibre Général Calculable (EGC)	Modèle d'espace d'états (Filtre de Kalman)	Autorégression Vectorielle Structurelle (SVAR)
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	2002	1990	1980
Auteur d'origine	Lofgren, Harris & Robinson (standard IFPRI CGE model in GAMS); Walrasian general equilibrium theory	Harvey; Durbin & Koopman (state space treatment); Kalman filter	Sims (1980); identification schemes by Blanchard & Quah (1989)
Type	Numerical general equilibrium model	State space time series model	Multivariate time series model
Source fondatrice	Lofgren, H., Harris, R.L. & Robinson, S. (2002). A Standard Computable General Equilibrium (CGE) Model in GAMS. IFPRI Microcomputers in Policy Research, 5.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
Alias	computable general equilibrium, applied general equilibrium model, Hesaplanabilir Genel Denge Modeli (CGE)	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)	SVAR, structural vector autoregression, identified VAR, structural VAR model
Apparentées	3	4	5
Résumé	A Computable General Equilibrium model is a numerical equilibrium framework that represents the input-output relationships among all sectors, factors of production, households, and foreign trade in an economy through a Social Accounting Matrix (SAM). Grounded in Walrasian general equilibrium theory and formalised in the standard IFPRI model of Lofgren, Harris and Robinson (2002), it simulates the economy-wide effects of policy shocks such as tax reform, trade liberalisation, or environmental policy.	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.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.