Seemingly unrelated regression model (SURE)

Before jumping to SURE or seemingly unrelated regression model, you need to understand following important terms:

• If two variables are associated with each other than this is called correlation.
• If two observation of a same variable are associated with each other than this is known as auto correlation.
• If in two variables auto correlation exist and these two variables are associated with each other, then cross correlation exists between the two variables.
• If two observation of independent variable are associated with each other than this is called Co-linear.

Seemingly unrelated regression model

Finally, if two observation of dependent variable are associated with each other than this is called SURE or seemingly unrelated regression model.

As per the above two equations, two dependent variable (CB & JB) has same independent variable (Z), then the two dependent variables are seemingly unrelated to each other. Therefore, this type of association of Seemingly unrelated equations variables is called SURE and the model is known as seemingly unrelated regression model.

On the other hand, as described in the above model, if two dependent variables are associated with each other, that is, shows the same trend and side by side their independent variable are dissimilar then this kind of association is known as Co-integration. Co-integration can be investigated through Johenson Cointegration test.

If we have to investigate trend of more then 1 equations and at the same time their independent variable are dissimilar, then the tool used is known as Co-integration. Whereas, if we want to investigate trend of one equation only, then the econometric tool deployed is known as Hodrick Prescott filter.