Compară metode

	Testul de cauzalitate Granger	Testul ARDL Bounds (Testul Pesaran Bounds)	Modelul Vectorial de Autoregresie (VAR)
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	1969	2001	2005
Autorul original	Clive W. J. Granger	Pesaran, Shin & Smith	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tip	Time-series predictive causality test	Cointegration test / Autoregressive distributed lag model	Multivariate time-series model
Sursa seminală	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Denumiri alternative	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Înrudite	5	4	4
Rezumat	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 ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations.	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).