Methoden vergleichen

	Vektorautoregressionsmodell (VAR)	ARIMA-Modell (Autoregressive Integrated Moving Average)	Vektor-Fehlerkorrekturmodell (VECM)
Fachgebiet	Ökonometrie	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	2005	2015	1987
Urheber	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition	Box & Jenkins (Box-Jenkins methodology)	Engle & Granger
Typ	Multivariate time-series model	Univariate time-series model	Multivariate time-series model
Wegweisende Quelle	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.	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	Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Aliasnamen	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli)
Verwandt	4	5	4
Zusammenfassung	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).	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).	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.