Comparer des méthodes

	Modèle de Moyenne Mobile Bayésienne (MA)	Modèle VAR bayésien (BVAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1970s–1997	1984
Auteur d'origine	Bayesian framework applied to Box-Jenkins MA models; West & Harrison (1997) canonical treatment	Doan, Litterman & Sims
Type	Bayesian time series model	Multivariate time-series model
Source fondatrice	West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Alias	Bayesian MA, Bayesian moving average, BMA time series, MA model with Bayesian estimation	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Apparentées	6	5
Résumé	The Bayesian MA model estimates a moving average time series model within a fully Bayesian framework, placing prior distributions on the MA parameters and error variance and updating them via Bayes' theorem. This approach yields full posterior distributions over model parameters and produces probabilistic forecasts with coherent uncertainty quantification.	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.