方法对比

	贝叶斯格兰杰因果关系	贝叶斯向量自回归模型 (BVAR)
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	1969 (frequentist); 1984 (Bayesian treatment)	1984
提出者	Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature	Doan, Litterman & Sims
类型	Bayesian causal inference test	Multivariate time-series model
开创性文献	Geweke, J. (1984). Inference and causality in economic time series models. Handbook of Econometrics, 2, 1101-1144. Elsevier.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
别名	Bayesian Granger test, Bayesian predictive causality, BGC, Bayesian causality in mean	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
相关	6	5
摘要	Bayesian Granger causality tests whether past values of one time series carry predictive information about another, framing the hypothesis through Bayesian inference rather than frequentist p-values. It combines a vector autoregressive (VAR) structure with prior distributions over coefficients and evaluates causal claims via posterior probabilities or Bayes factors, providing a probabilistic and nuanced alternative to the classical Granger test.	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.