Comparer des méthodes

	Modèle autorégressif (AR) bayésien	Modèle VAR bayésien (BVAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1971	1984
Auteur d'origine	Arnold Zellner; foundational Bayesian time-series work by West & Harrison	Doan, Litterman & Sims
Type	Bayesian time-series model	Multivariate time-series model
Source fondatrice	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Alias	Bayesian autoregressive model, BAR model, Bayesian AR, Bayesian time-series autoregression	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Apparentées	6	5
Résumé	The Bayesian AR model estimates an autoregressive time-series process by combining a likelihood derived from the AR structure with prior distributions over the lag coefficients and error variance. Rather than producing single point estimates, it yields full posterior distributions, enabling principled uncertainty quantification and probabilistic forecasting.	The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.