Porovnat metody

	Vektorová autoregrese (VAR)	Model ARMA (Autoregressive Moving Average)	Strukturální vektorová autoregrese (SVAR)
Obor	Ekonometrie	Ekonometrie	Ekonometrie
Rodina	Regression model	Regression model	Regression model
Rok vzniku	1980	1970	1980
Tvůrce	Christopher A. Sims	George E. P. Box and Gwilym M. Jenkins	Sims (1980); identification schemes by Blanchard & Quah (1989)
Typ	Multivariate time-series model	Time series model	Multivariate time series model
Původní zdroj	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.
Další názvy	VAR, VAR model, vector autoregressive model, multivariate autoregression	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	SVAR, structural vector autoregression, identified VAR, structural VAR model
Příbuzné	5	5	5
Shrnutí	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 ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.	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.