方法对比

	可计算一般均衡（CGE）模型	普通最小二乘法 (OLS) 回归	向量自回归 (VAR) 模型
领域	计量经济学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model
起源年份	2002	2019	2005
提出者	Lofgren, Harris & Robinson (standard IFPRI CGE model in GAMS); Walrasian general equilibrium theory	Wooldridge (textbook treatment); classical least squares	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
类型	Numerical general equilibrium model	Linear regression	Multivariate time-series model
开创性文献	Lofgren, H., Harris, R.L. & Robinson, S. (2002). A Standard Computable General Equilibrium (CGE) Model in GAMS. IFPRI Microcomputers in Policy Research, 5.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
别名	computable general equilibrium, applied general equilibrium model, Hesaplanabilir Genel Denge Modeli (CGE)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
相关	3	5	4
摘要	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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).