Is Multicollinearity a Problem for Prediction?

Multicollinearity undermines the statistical significance of an independent variable. Here it is important to point out that multicollinearity does not affect the model's predictive accuracy. The model should still do a relatively decent job predicting the target variable when multicollinearity is present.

Why does multicollinearity not affect prediction?

If the covariance structure (and consequently the multicollinearity) is similar in both training and test datasets, then it does not pose a problem for prediction. Since a test dataset is typically a random subset of the full dataset, it's generally reasonable to assume that the covariance structure is the same.

Why is multicollinearity not a problem?

It increases the standard errors of their coefficients, and it may make those coefficients unstable in several ways. But so long as the collinear variables are only used as control variables, and they are not collinear with your variables of interest, there's no problem.