方法对比

	贝叶斯向量自回归模型 (BVAR)	贝叶斯向量误差修正模型 (Bayesian VECM)
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	1984	2002–2005
提出者	Doan, Litterman & Sims	Kleibergen & Paap; Villani
类型	Multivariate time-series model	Bayesian multivariate time series model
开创性文献	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.
别名	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction
相关	5	5
摘要	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.