Compară metode

	Modelul Vectorial de Autoregresie (VAR)	Testul ARDL Bounds (Testul Pesaran Bounds)	Modelul ARIMA (Autoregresiv Integrat cu Medii Mobile)
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	2005	2001	2015
Autorul original	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition	Pesaran, Shin & Smith	Box & Jenkins (Box-Jenkins methodology)
Tip	Multivariate time-series model	Cointegration test / Autoregressive distributed lag model	Univariate time-series model
Sursa seminală	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.	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.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021
Denumiri alternative	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon	Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Înrudite	4	4	5
Rezumat	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).	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).