Bandingkan metode

	Model Koreksi Galat Vektor (VECM)	Regresi Kuadrat Terkecil Biasa (Ordinary Least Squares - OLS)	Model Autoregresi Vektor (VAR)
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1987	2019	2005
Pencetus	Engle & Granger	Wooldridge (textbook treatment); classical least squares	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tipe	Multivariate time-series model	Linear regression	Multivariate time-series model
Sumber perintis	Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Terkait	4	5	4
Ringkasan	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.	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).	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).