Comparar métodos

	Modelo ARMA (Média Móvel Autorregressiva)	Granger Causality Test	Autoregressores Vetoriais Estruturais (SVAR)
Área	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Ano de origem	1970	1969	1980
Autor original	George E. P. Box and Gwilym M. Jenkins	Clive W. J. Granger	Sims (1980); identification schemes by Blanchard & Quah (1989)
Tipo	Time series model	Causality test (F-test on VAR)	Multivariate time series model
Fonte seminal	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
Outros nomes	ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q)	Granger test, GC test, predictive causality test, Granger non-causality test	SVAR, structural vector autoregression, identified VAR, structural VAR model
Relacionados	5	5	5
Resumo	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.	The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.	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.