Methoden vergleichen

	Vektorautoregression (VAR)	ARIMA-Modell (Autoregressives integriertes gleitendes Durchschnittsmodell)	Strukturelle Vektorautoregression (SVAR)
Fachgebiet	Ökonometrie	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	1980	1970	1980
Urheber	Christopher A. Sims	George Box and Gwilym Jenkins	Sims (1980); identification schemes by Blanchard & Quah (1989)
Typ	Multivariate time-series model	Time series forecasting model	Multivariate time series model
Wegweisende Quelle	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
Aliasnamen	VAR, VAR model, vector autoregressive model, multivariate autoregression	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	SVAR, structural vector autoregression, identified VAR, structural VAR model
Verwandt	5	6	5
Zusammenfassung	Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.