방법 비교

	베이지안 분위-분위 회귀분석(Bayesian Quantile-on-Quantile Regression)	베이즈 VAR 모형 (BVAR)
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	2015–2019	1984
창시자	Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001)	Doan, Litterman & Sims
유형	Nonparametric quantile regression with Bayesian estimation	Multivariate time-series model
원전	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
별칭	Bayesian QQR, Bayesian QQ regression, Bayes quantile-on-quantile, BQQ regression	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
관련	6	5
요약	Bayesian Quantile-on-Quantile (BQQ) Regression extends the Sim-Zhou quantile-on-quantile framework by replacing frequentist local linear estimation with Bayesian posterior inference. For each pair of quantiles (theta of the outcome, tau of the predictor), the method yields a full posterior distribution over the slope, enabling uncertainty quantification across the entire bivariate quantile surface — a key advantage when sample sizes are moderate and tail quantiles are sparse.	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.