পদ্ধতির তুলনা করুন

	কম্পিউটেবল জেনারেল ইকুইলিব্রিয়াম (CGE) মডেল	স্টেট স্পেস মডেল (কালম্যান ফিল্টার)	কাঠামোগত ভেক্টর অটোরিগ্রেশন (SVAR)
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	2002	1990	1980
প্রবর্তক	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)
ধরন	Numerical general equilibrium model	State space time series model	Multivariate time series model
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	3	4	5
সারসংক্ষেপ	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.