方法对比

	贝叶斯向量误差修正模型 (Bayesian VECM)	结构向量自回归 (SVAR)
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	2002–2005	1980
提出者	Kleibergen & Paap; Villani	Sims (1980); identification schemes by Blanchard & Quah (1989)
类型	Bayesian multivariate time series model	Multivariate time series model
开创性文献	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.
别名	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction	SVAR, structural vector autoregression, identified VAR, structural VAR model
相关	5	5
摘要	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.