Comparar métodos

	Causalidade de Granger Bayesiana	Granger Causality Test
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1969 (frequentist); 1984 (Bayesian treatment)	1969
Autor original	Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature	Clive W. J. Granger
Tipo	Bayesian causal inference test	Causality test (F-test on VAR)
Fonte seminal	Geweke, J. (1984). Inference and causality in economic time series models. Handbook of Econometrics, 2, 1101-1144. Elsevier.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.
Outros nomes	Bayesian Granger test, Bayesian predictive causality, BGC, Bayesian causality in mean	Granger test, GC test, predictive causality test, Granger non-causality test
Relacionados	6	5
Resumo	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 Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.