Сравнение методов

	Модель Байесовского векторного авторегрессионного анализа (BVAR)	Байесовская модель векторной коррекции ошибок (Bayesian VECM)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1984	2002–2005
Автор метода	Doan, Litterman & Sims	Kleibergen & Paap; Villani
Тип	Multivariate time-series model	Bayesian 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.	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.
Другие названия	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction
Связанные	5	5
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.	The Bayesian VECM combines the classical Vector Error Correction Model — which captures both short-run dynamics and long-run cointegrating relationships among non-stationary multivariate time series — with Bayesian prior distributions over the cointegrating rank and coefficient matrices. This allows principled uncertainty quantification, incorporation of economic theory as priors, and coherent inference even in small samples.