Comparer des méthodes

	Autoregression vectorielle bayésienne (BVAR)	Modèle de Vector Autoregression (VAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1986	2005
Auteur d'origine	Litterman (1986); Bańbura, Giannone & Reichlin (2010)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Type	Bayesian multivariate time-series model	Multivariate time-series model
Source fondatrice	Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Apparentées	5	4
Résumé	Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).