Módszerek összehasonlítása

	ARDL Határvizsgálat (Pesaran Határvizsgálat)	Kointegrációs teszt (Johansen / Engle-Granger)	Vektor Autoregressziós (VAR) Modell
Tudományterület	Ökonometria	Ökonometria	Ökonometria
Módszercsalád	Regression model	Regression model	Regression model
Keletkezés éve	2001	1988	2005
Megalkotó	Pesaran, Shin & Smith	Engle & Granger (1987); Johansen (1988)	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Típus	Cointegration test / Autoregressive distributed lag model	Time-series cointegration test	Multivariate time-series model
Alapmű	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.	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.
Alternatív nevek	Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)	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
Kapcsolódó	4	5	4
Összefoglaló	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.	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).