Multicollinearity can lead to unreliable regression results and should be addressed from "summary" of Introduction to Econometrics by Christopher Dougherty
Multicollinearity refers to the situation where two or more independent variables in a regression model are highly correlated with each other. When multicollinearity is present, it can lead to unreliable results in the regression analysis. This is because it becomes difficult for the regression model to separate the individual effects of the highly correlated variables on the dependent variable. In the presence of multicollinearity, the estimated coefficients of the correlated variables may be unstable and have large standard errors. This means that the coefficients may not accurately represent the true relationship between the independent variables and the dependent variable. As a result, the statistical significance of the coefficients may be distorted, leading to incorrect infere...
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