Comparar métodos

	Teste de Causalidade de Granger	Teste de Cointegração (Johansen / Engle-Granger)	Modelo de Vetores Autorregressivos (VAR)
Área	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Ano de origem	1969	1988	2005
Autor original	Clive W. J. Granger	Engle & Granger (1987); Johansen (1988)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tipo	Time-series predictive causality test	Time-series cointegration test	Multivariate time-series model
Fonte seminal	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Outros nomes	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	Johansen cointegration test, Engle-Granger cointegration test, long-run equilibrium test, Eşbütünleşme Testi (Johansen/Engle-Granger)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Relacionados	5	5	4
Resumo	The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.	The cointegration test examines whether non-stationary time series that each contain a unit root share a stable long-run equilibrium relationship. The single-equation residual approach was introduced by Engle and Granger (1987) and the system-based rank approach by Johansen (1988).	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).