方法对比

	局部投影	门限面板向量自回归模型	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	2005	1996	2005
提出者	Oscar Jorda	Bruce Hansen and colleagues	Bernanke, Boivin, and Eliasz
类型	Multi-horizon regression	Nonlinear panel model	Time-varying system
开创性文献	Jorda, O. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182.	Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometric Theory, 12(3), 386-414.	Bernanke, B. S., Boivin, J., & Eliasz, P. S. (2005). Measuring monetary policy. Journal of Political Economy, 113(1), 161-208.
别名	LP-IR, Multi-horizon regression	Panel-VAR with regime switching	Dynamic factor model with time-varying parameters
相关	3	3	3
摘要	Local Projections (LP) is a semi-parametric method for estimating impulse responses directly via multi-horizon regressions, bypassing VAR-model specification. Introduced by Jorda (2005), it projects outcomes h periods ahead onto current shocks and lags, producing impulse-response functions without assuming a particular lag structure or VAR order. This flexibility has made it the dominant approach in applied macroeconomics for measuring policy effects and shock transmission.	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.	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.