Good day Researchers!
I am running a Panel data and I just finished with the unit root tests using Fisher(ADF and PP) and LLC. My dependent variables are not stationary at level but stationary at first difference for the Fisher, while in LLC one of the dependent variable is stationary at level and difference.
While my independent variables results is mixed. Some are stationary at first difference, while some are stationary at both but one of the independent variable is not stationary at both level and difference.
Should I still run a Cointegration test?
Dear Toyin Oluwatuyi
It is common to have the problem of spurious regression in the modelling of variables, because of a linear regression on non-stationary variables, which in principle attests to a very appreciable R² and Student's t explanatory mechanism, while in reality there is no relationship between them.
The objective of cointegration is to be able to determine a stationary residual by working on two variables that are not stationary in level. The idea proposed by Engle is that in the short term the variables diverge, but that in the long term there is a stability, an equilibrium between them. A common evolution of the variables.
For more explanation I leave you the link which will bring you back to my article on the principle of cointegration.
Article Integration and Cointegration of Variables (ICV)
Warm Regards,
co integration analysis it is important to do unit root analysis to identify the stationary and order of integration. Using Augmented Dickey Fuller tests and KPSS Test. The results reveal that all the series are non stationary at levels as test statistics are greater than the critical values. However by taking the first
difference all the series are found to be non-stationary. then by taking the second order differential all series are found be Stationary. Thus the null hypothesis is rejected and the log values of future, spot prices are found to be stationary at Second difference and are integrated in the order of two.
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