Sammenlign metoder

	Dynamisk stokastisk generell likevektsmodell (DSGE-modell)	Beregningsbar generell likevektsmodell (CGE)	Tilstandsrommodell (Kalmanfilter)	Strukturell Vektor Autoregresjon (SVAR)	Vektor Autoregression (VAR)-modell
Fagfelt	Økonometri	Økonometri	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model	Regression model	Regression model
Opprinnelsesår	2007	2002	1990	1980	2005
Opphavsperson	Smets & Wouters; An & Schorfheide (Bayesian DSGE estimation)	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)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Type	Micro-founded macroeconomic general equilibrium model	Numerical general equilibrium model	State space time series model	Multivariate time series model	Multivariate time-series model
Opprinnelig kilde	Smets, F. & Wouters, R. (2007). Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review, 97(3), 586–606.	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.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	DSGE, dynamic stochastic general equilibrium, micro-founded macroeconomic model, Dinamik Stokastik Genel Denge Modeli (DSGE)	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	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Relaterte	5	3	4	5	4
Sammendrag	A DSGE model is a micro-founded macroeconomic general equilibrium model that combines the optimising decisions of households, firms, and government under rational expectations. Popularised for empirical policy work by Smets and Wouters (2007) and given its Bayesian estimation framework by An and Schorfheide (2007), it is the standard tool for central-bank policy analysis, fiscal-shock simulation, and the study of business-cycle fluctuations.	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.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).