(Granger, 1969, 1988), where it can be addressed in terms of a VAR (vector auto regression) system. If an export platform is important for the country, FDI inflows should result in an increase in export flows from the host country. Therefore, Granger causality could be used to examine whether FDI Granger inflows cause export flows.

The direction of the Granger causality is sensitive to the number of lags; therefore, it is important to use the Akaike Information Criterion (AIC) (Akaike, 1974) to suggest the use of the lag with the lowest computed AIC value. The AIC is defined as: ln AIC = [ 2k/n] + ln [ RSS/n] where k is the number of regressors, n is the number of observations, and RSS is the residual sum of squares.

For FDI and exports and FDI and imports, the Granger causality test is applied based on a stationary dataset. Hence, it is necessary to establish the stationarity properties of the data, and unit root analysis is conducted for this purpose.  If an export platform is important for the country, the result will indicate an increase in export flows from the technology sector. Therefore, Granger causality could be used to examine whether FDI Granger inflows cause export flows or import substitution from the technology sector. The direction of the Granger causality is sensitive to the number of lags; therefore, it is important to use the Akaike Information Criterion (AIC) (Akaike, 1974) to suggest the use of the lag with the lowest computed AIC value. Given a data set, several competing models may be ranked according to their AIC, with the one having the lowest AIC being the best.