方法对比

	门限面板向量自回归模型	全球向量自回归模型	时变参数因子增强向量自回归模型
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	1996	2004	2005
提出者	Bruce Hansen and colleagues	Pesaran, Schuermann, and Weiner	Bernanke, Boivin, and Eliasz
类型	Nonlinear panel model	International system model	Time-varying system
开创性文献	Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometric Theory, 12(3), 386-414.	Pesaran, M. H., Schuermann, T., & Weiner, S. M. (2004). Modeling regional interdependencies using a global error-correcting macroeconometric model. Journal of Business and Economic Statistics, 22(2), 129-162.	Bernanke, B. S., Boivin, J., & Eliasz, P. S. (2005). Measuring monetary policy. Journal of Political Economy, 113(1), 161-208.
别名	Panel-VAR with regime switching	GVAR, Multi-country VAR	Dynamic factor model with time-varying parameters
相关	3	3	3
摘要	The Threshold Panel VAR extends the standard vector autoregression framework to accommodate regime-switching behavior where relationships change when a threshold variable crosses a critical level. Introduced by Hansen (1996) and applied to panels by Caner and Hansen (2001), it allows different dynamic relationships across regimes (e.g., expansions versus recessions) while exploiting the cross-sectional dimension of panel data. This nonlinear framework captures state-dependent policy effects and economic mechanisms.	Global VAR (GVAR) is a large-scale macroeconomic modeling framework linking multiple countries (or regions) via trade and financial channels, allowing shocks in one country to propagate through the global system. Introduced by Pesaran et al. (2004), it solves the curse of dimensionality in international VAR models by estimating country-specific VARs conditional on foreign variables, then solving a system linking all countries. This approach is invaluable for analyzing global spillovers and international policy coordination.	TVP-FAVAR is a hybrid framework combining factor-augmented VARs with time-varying parameter estimation via Kalman filtering. Introduced by Bernanke et al. (2005) and refined by Primiceri (2005), it extracts latent economic factors (e.g., a 'common monetary policy shock') from high-dimensional data while allowing VAR coefficients to evolve stochastically over time. This framework captures both reduced-dimensionality patterns and structural instability, making it ideal for studying evolving policy regimes and shock dynamics.