Dear friends;
I have a doubt that why we employ the cointegration technique to test the long - run relationship between the variables. Can't we use the regression technique to test the long - run relationship between the variables? what is the difference between both techniques
Hi ahamed.
When it comes to a shock variable, if the shock variable is intact, it is stationary, If the shock is not drained, it is non-stationary.
This topic is related to the unit root. It is important that a variable has unit root or not. If it had no root root, it would have no problem.
But if it had the unit root , one way is to use a variable difference in the estimate. The second way, which is better, is to examine the long-term relationship of variables by cointegration techniques. If there was an cointegration then there is no need to enter the first or second order difference of the variables in the model.
In the time series data for cointegration tests, Granger and Johansen can be used. In the data panel, you can use the methods of Fisher, kao and Pedroni.
but the fmols and dols method in panel data can indicate the Existence and give the long-run coefficient.
good luck
Cointegration is used if the variables are integrated at order 1. Regression is used if the variables do not have unit root.
Co-Integration is used for investigate correlation in non stationary variables and long run impact of explanatory variable on dependent variable . Co-Integration found good to check biggest sample set and to check variables which have similar stochastic trend.
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