Usporedite metode

	Model Vektorske Autoregresije (VAR)	ARDL test granica (Pesaranov test granica)	Regresija običnih najmanjih kvadrata (OLS)
Područje	Ekonometrija	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model	Regression model
Godina nastanka	2005	2001	2019
Tvorac	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition	Pesaran, Shin & Smith	Wooldridge (textbook treatment); classical least squares
Vrsta	Multivariate time-series model	Cointegration test / Autoregressive distributed lag model	Linear regression
Temeljni izvor	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Drugi nazivi	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon	Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Srodne	4	4	5
Sažetak	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).