Сравнение методов

	Байесовская модель векторной коррекции ошибок (Bayesian VECM)	Модель Байесовского векторного авторегрессионного анализа (BVAR)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	2002–2005	1984
Автор метода	Kleibergen & Paap; Villani	Doan, Litterman & Sims
Тип	Bayesian multivariate time series model	Multivariate time-series model
Основополагающий источник	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Другие названия	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Связанные	5	5
Сводка	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.	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.