Salīdzināt metodes

	Panel VAR ar sliekšņa vērtību	Regresija ar gludu pāreju paneļos	TVP-FAVAR
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1996	2005	2005
Autors	Bruce Hansen and colleagues	Gonzalez, Terasvirta, and van Dijk	Bernanke, Boivin, and Eliasz
Tips	Nonlinear panel model	Smooth-regime panel model	Time-varying system
Pirmavots	Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometric Theory, 12(3), 386-414.	Gonzalez, A., Terasvirta, T., & van Dijk, D. (2005). Panel smooth transition regression models. Research Paper, Melbourne Institute of Applied Economic and Social Research.	Bernanke, B. S., Boivin, J., & Eliasz, P. S. (2005). Measuring monetary policy. Journal of Political Economy, 113(1), 161-208.
Citi nosaukumi	Panel-VAR with regime switching	Smooth-transition panel model	Dynamic factor model with time-varying parameters
Saistītās	3	3	3
Kopsavilkums	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.	Panel Smooth Transition Regression (PSTR) models nonlinear panel relationships where coefficients transition smoothly (rather than abruptly) between regimes as a transition variable crosses thresholds. Introduced by Gonzalez et al. (2005), it extends univariate smooth-transition autoregression (STAR) models to panels, capturing gradual shifts in economic behavior. This approach is realistic when adjustment costs cause smooth (not sudden) regime changes.	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.