Comparar métodos

	Autoregressores Vetoriais (VAR)	Modelo ARIMA (Autoregressive Integrated Moving Average)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1980	1970
Autor original	Christopher A. Sims	George Box and Gwilym Jenkins
Tipo	Multivariate time-series model	Time series forecasting model
Fonte seminal	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.
Outros nomes	VAR, VAR model, vector autoregressive model, multivariate autoregression	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Relacionados	5	6
Resumo	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.