Comparer des méthodes

	Modèle Bayésien de Correction d'Erreur Vectoriel (Bayesian VECM)	Autorégression Vectorielle Structurelle (SVAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2002–2005	1980
Auteur d'origine	Kleibergen & Paap; Villani	Sims (1980); identification schemes by Blanchard & Quah (1989)
Type	Bayesian multivariate time series model	Multivariate time series model
Source fondatrice	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
Alias	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction	SVAR, structural vector autoregression, identified VAR, structural VAR model
Apparentées	5	5
Résumé	The Bayesian VECM combines the classical Vector Error Correction Model — which captures both short-run dynamics and long-run cointegrating relationships among non-stationary multivariate time series — with Bayesian prior distributions over the cointegrating rank and coefficient matrices. This allows principled uncertainty quantification, incorporation of economic theory as priors, and coherent inference even in small samples.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.