Сравнение методов

	Байесовская причинность по Грейнджеру	Модель Байесовского векторного авторегрессионного анализа (BVAR)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1969 (frequentist); 1984 (Bayesian treatment)	1984
Автор метода	Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature	Doan, Litterman & Sims
Тип	Bayesian causal inference test	Multivariate time-series model
Основополагающий источник	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.
Другие названия	Bayesian Granger test, Bayesian predictive causality, BGC, Bayesian causality in mean	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Связанные	6	5
Сводка	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.