Compară metode

	Modelul ARMA bayesian	Modelul Vector Autoregresiv Bayesian (BVAR)
Domeniu	Econometrie	Econometrie
Familie	Regression model	Regression model
Anul apariției	1970s–1980s	1984
Autorul original	Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s	Doan, Litterman & Sims
Tip	Bayesian time series model	Multivariate time-series model
Sursa seminală	Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Denumiri alternative	Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Înrudite	6	5
Rezumat	The Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses.	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.