Comparer des méthodes

	Régression de Fama-MacBeth	VARX de panel	VAR à facteurs augmentés à paramètres variant dans le temps
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	1973	2013	2005
Auteur d'origine	Eugene Fama and James MacBeth	Canova and Ciccarelli	Bernanke, Boivin, and Eliasz
Type	Cross-sectional regression	Multi-equation panel model	Time-varying system
Source fondatrice	Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607-636.	Canova, F., & Ciccarelli, M. (2013). Panel vector autoregressive models: A survey. Advances in Econometrics, 32, 205-246.	Bernanke, B. S., Boivin, J., & Eliasz, P. S. (2005). Measuring monetary policy. Journal of Political Economy, 113(1), 161-208.
Alias	Two-step cross-sectional regression	Panel VAR-X	Dynamic factor model with time-varying parameters
Apparentées	3	3	3
Résumé	The Fama-MacBeth procedure is a two-step regression methodology for analyzing cross-sectional relationships while controlling for time-series structure. Introduced by Fama and MacBeth (1973), it first estimates time-series parameters for each cross-sectional unit, then regresses outcomes on those parameters across the cross-section, averaging results over time. This approach elegantly separates within-unit dynamics from cross-sectional heterogeneity and provides standard errors robust to panel structure.	Panel VARX extends vector autoregression to heterogeneous panels with exogenous variables, enabling simultaneous modeling of multiple endogenous variables alongside observed external factors across many units. Introduced by Holtz-Eakin et al. (1988) and advanced by Canova and Ciccarelli (2013), it captures dynamic relationships within units while allowing parameters to vary across units. This framework is essential for macroeconomic panels and understanding cross-unit heterogeneity in responses to common shocks.	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.