Question

Suggest one way each to deal with the problems of endogeneity and multicollinearity.

Answer 1

For endogeneity :

Ad Hoc Solutions -

:- if a dependent variable is potentially endogenous, it is intuitively appealing to look for a proxy that does not suffer from the same problem.

:- the most common approach is to lag the suspect variables by one or more periods.

:- the argument is that although current values of (eg) tariffs might be endogenous to import penetration, it is unlikely that past values of tariffs are subject to the same problem.

For multicollinearity :

Remove highly correlated predictors from the model. If you have two or more factors with a high VIF, remove one from the model. Because they supply redundant information, removing one of the correlated factors usually doesn't drastically reduce the R-squared. Consider using stepwise regression, best subsets regression, or specialized knowledge of the data set to remove these variables. Select the model that has the highest R-squared value.