השוואת שיטות

	מודל וקטור אוטורגרסיבי בייסיאני (BVAR)	מודל VAR פורייה
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1984	2010s
הוגה השיטה	Doan, Litterman & Sims	Enders & Lee; extended by Nazlioglu and others to VAR systems
סוג	Multivariate time-series model	Multivariate time-series model
מקור מכונן	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.
כינויים	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR
קשורות	5	6
תקציר	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.