A model suffer from omitted variable bias if a variable that is correlated with both the dependant variable and the explanatory variable is excluded.
A model suffer from multicollinearity if two explanatory variables are correlated.
So which one is more important?
I have tryed to run a regression with panel data were health care expenditure (HCE) is my dependant variable and GDP.
If average wages is a determinant of HCE and correlated with GDP it is wrong to ommit it.
But if I include it, then mye model haster a high degree of multicollinearity between wages and GDP.
So omitted variable bias and multicollinearity contradict each other?
A model suffer from multicollinearity if two explanatory variables are correlated.
So which one is more important?
I have tryed to run a regression with panel data were health care expenditure (HCE) is my dependant variable and GDP.
If average wages is a determinant of HCE and correlated with GDP it is wrong to ommit it.
But if I include it, then mye model haster a high degree of multicollinearity between wages and GDP.
So omitted variable bias and multicollinearity contradict each other?
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