Vertaile menetelmiä

	ARIMA-malli (Autoregressiivinen integroitu liukuva keskiarvo)	Granger-kausaalisuustesti
Tieteenala	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1970	1969
Kehittäjä	George Box and Gwilym Jenkins	Clive W. J. Granger
Tyyppi	Time series forecasting model	Causality test (F-test on VAR)
Alkuperäislähde	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.
Rinnakkaisnimet	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	Granger test, GC test, predictive causality test, Granger non-causality test
Liittyvät	6	5
Tiivistelmä	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.	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.