So sánh phương pháp

	Mô hình Vector Tự hồi quy Bayes (BVAR)	Mô hình VAR Fourier
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1984	2010s
Người khởi xướng	Doan, Litterman & Sims	Enders & Lee; extended by Nazlioglu and others to VAR systems
Loại	Multivariate time-series model	Multivariate time-series model
Công trình gốc	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.	Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.
Tên gọi khác	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR
Liên quan	5	6
Tóm tắt	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.	The Fourier VAR model extends the standard Vector Autoregression by replacing fixed deterministic terms with Fourier trigonometric components, allowing the intercept (and optionally the trend) to shift gradually and smoothly over time. This eliminates the need to pre-specify the number, timing, or shape of structural breaks in a multivariate time-series system.