Since multicollinearity causes imprecise estimates of coefficient values, the resulting out-of-sample predictions will also be imprecise. And if the pattern of multicollinearity in the new data differs from that in the data that was fitted, such extrapolation may introduce large errors in the predictions.

Does collinearity affect prediction?

Multicollinearity affects the coefficients and p-values, but it does not influence the predictions, precision of the predictions, and the goodness-of-fit statistics.

What are the effects of collinearity?

1. Statistical consequences of multicollinearity include difficulties in testing individual regression coefficients due to inflated standard errors. Thus, you may be unable to declare an X variable significant even though (by itself) it has a strong relationship with Y. 2.