Vertaile menetelmiä

	Vektorivirheenkorjausmalli (VECM)	ARIMA (Autoregressive Integrated Moving Average) -malli	OLS-regressio (Ordinary Least Squares)
Tieteenala	Ekonometria	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1987	2015	2019
Kehittäjä	Engle & Granger	Box & Jenkins (Box-Jenkins methodology)	Wooldridge (textbook treatment); classical least squares
Tyyppi	Multivariate time-series model	Univariate time-series model	Linear regression
Alkuperäislähde	Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	4	5	5
Tiivistelmä	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.	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).	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).