Сравнение методов

	Байесовская модель структурной векторной авторегрессии (B-SVAR)	Байесовская модель векторной коррекции ошибок (Bayesian VECM)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1998–2005	2002–2005
Автор метода	Sims & Zha (1998); Uhlig (2005) for sign-restriction identification	Kleibergen & Paap; Villani
Тип	Structural multivariate time-series model	Bayesian multivariate time series model
Основополагающий источник	Sims, C. A., & Zha, T. (1998). Bayesian methods for dynamic multivariate models. International Economic Review, 39(4), 949–968.	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	Bayesian SVAR, B-SVAR, Bayesian structural VAR, Bayesian identified VAR	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction
Связанные	6	5
Сводка	The Bayesian Structural Vector Autoregression model combines the structural identification of SVAR with Bayesian prior distributions over parameters. It estimates causal impulse responses between multiple time series while incorporating prior economic knowledge and producing full posterior uncertainty bands rather than point estimates alone.	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.