Methoden vergelijken

	Coïntegratietest (Johansen / Engle-Granger)	Granger-causaliteitstest	Vector Autoregressie (VAR)-model
Vakgebied	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Jaar van ontstaan	1988	1969	2005
Grondlegger	Engle & Granger (1987); Johansen (1988)	Clive W. J. Granger	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Type	Time-series cointegration test	Time-series predictive causality test	Multivariate time-series model
Oorspronkelijke bron	Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Aliassen	Johansen cointegration test, Engle-Granger cointegration test, long-run equilibrium test, Eşbütünleşme Testi (Johansen/Engle-Granger)	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Verwant	5	5	4
Samenvatting	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).	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.	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).