Methoden vergelijken

	Vector Autoregressie (VAR)-model	Gewone Kleinste Kwadraten (GKK) Regressie	Vector Error Correction Model (VECM)
Vakgebied	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Jaar van ontstaan	2005	2019	1987
Grondlegger	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition	Wooldridge (textbook treatment); classical least squares	Engle & Granger
Type	Multivariate time-series model	Linear regression	Multivariate time-series model
Oorspronkelijke bron	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Aliassen	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli)
Verwant	4	5	4
Samenvatting	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).	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).	The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework.