방법 비교

	베이지안 그레인저 인과관계(Bayesian Granger Causality)	Vector Autoregression (VAR)
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	1969 (frequentist); 1984 (Bayesian treatment)	1980
창시자	Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature	Christopher A. Sims
유형	Bayesian causal inference test	Multivariate time-series model
원전	Geweke, J. (1984). Inference and causality in economic time series models. Handbook of Econometrics, 2, 1101-1144. Elsevier.	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.
별칭	Bayesian Granger test, Bayesian predictive causality, BGC, Bayesian causality in mean	VAR, VAR model, vector autoregressive model, multivariate autoregression
관련	6	5
요약	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.	Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance.