Sammenlign metoder

	Granger-kausalitetstest	ARIMA-modellen (Autoregressive Integrated Moving Average)
Fagområde	Økonometri	Økonometri
Familie	Regression model	Regression model
Oprindelsesår	1969	1970
Ophavsperson	Clive W. J. Granger	George Box and Gwilym Jenkins
Type	Causality test (F-test on VAR)	Time series forecasting model
Oprindelig kilde	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Aliasser	Granger test, GC test, predictive causality test, Granger non-causality test	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Relaterede	5	6
Resumé	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.	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.