Сравнение методов

	Модель Байесовского векторного авторегрессионного анализа (BVAR)	Модель векторной авторегрессии (VAR)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1984	2005
Автор метода	Doan, Litterman & Sims	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Тип	Multivariate time-series model	Multivariate time-series model
Основополагающий источник	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.
Другие названия	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Связанные	5	4
Сводка	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).