Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Effets Fixes Interactifs× | VAR à facteurs augmentés à paramètres variant dans le temps× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2009 | 2005 |
| Auteur d'origine≠ | Jushan Bai | Bernanke, Boivin, and Eliasz |
| Type≠ | Panel with latent structure | Time-varying system |
| Source fondatrice≠ | Bai, J. (2009). Panel data models with interactive fixed effects. Econometric Reviews, 28(4), 289-312. link ↗ | Bernanke, B. S., Boivin, J., & Eliasz, P. S. (2005). Measuring monetary policy. Journal of Political Economy, 113(1), 161-208. link ↗ |
| Alias | Factor models with individual heterogeneity | Dynamic factor model with time-varying parameters |
| Apparentées | 3 | 3 |
| Résumé≠ | Interactive Fixed Effects (IFE) extends standard fixed-effects panel models by allowing unit-specific intercepts to vary not just at the individual level but also with unobserved common time-varying factors. Introduced by Bai (2009), it models heterogeneity as the interaction of individual characteristics and common shocks, ideal for studying cross-sectional variation in how units respond to macro conditions. This framework dominates when common factors drive substantial heterogeneity. | 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. |
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