Сравнение на методи

	Байесов модел за корекция на грешки във векторна форма (Bayesian VECM)	Структурна векторна авторегресия (SVAR)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2002–2005	1980
Създател	Kleibergen & Paap; Villani	Sims (1980); identification schemes by Blanchard & Quah (1989)
Тип	Bayesian multivariate time series model	Multivariate time series model
Основополагащ източник	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
Други названия	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction	SVAR, structural vector autoregression, identified VAR, structural VAR model
Свързани	5	5
Резюме	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.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.