방법 비교

	베이즈 ARMA 모형	베이즈 VAR 모형 (BVAR)
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	1970s–1980s	1984
창시자	Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s	Doan, Litterman & Sims
유형	Bayesian time series model	Multivariate time-series model
원전	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.
별칭	Bayesian ARMA, B-ARMA, Bayesian autoregressive moving average, ARMA with Bayesian inference	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
관련	6	5
요약	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.