Yt — dependent variable
xt — independent variable
β — regression parameters
μt —Random error
This research will form a model of regression on Gross Domestic Product (GDP) per capita,
with, investment rate (I), net export (NX), inflation rate (CPI), and unemployment rate (UER)
by using OLS in E-views software.
3.5.2 Diagnostic Checking
Multicollinearity exists whenever an independent variable is highly correlated with other
independent variables in a multiple regression equation. According to Farrar (1967) If the t-
test is most insignificant for all parameters, the F-test is significant, the R-squared is high,
then multicollinearity issues will occur. However, in a regression model, there are different
approaches to detect multicollinearity.
Firstly, the dependent variable has a few important independent variable effects, but the
performance of the regression model indicates that the model has a high R-squared. Thus, the
model could have a problem with multicollinearity.,
Secondly, when predictors are added, substantial changes in coefficients can occur when the
predictors are fully independent of each other when adding or removing one, their
coefficients will not change at all. However, the more they interfere, the more their
coefficients can change dramatically.
Thirdly, The Variance Inflation Factor (VIF) is a method used to assess the severity of
multicollinearity for the identification of the existence of a multicollinearity defect.