방법 비교

	베이즈 VAR 모형 (BVAR)	Vector Autoregression (VAR) Model
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	1984	2005
창시자	Doan, Litterman & Sims	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
유형	Multivariate time-series model	Multivariate time-series model
원전	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
별칭	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
관련	5	4
요약	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.	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).