方法对比

	可计算一般均衡（CGE）模型	面板数据固定效应模型	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	2014	2005
提出者	Lofgren, Harris & Robinson (standard IFPRI CGE model in GAMS); Walrasian general equilibrium theory	Hsiao (textbook treatment); within transformation of panel data	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
类型	Numerical general equilibrium model	Panel data 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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.	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)	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	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.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).	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).