What is typically the effect of high multicollinearity on a regression model?

High multicollinearity in a regression model refers to a situation where two or more independent variables are highly correlated, meaning they contain similar information about the variance in the dependent variable. This condition can lead to unstable and unreliable coefficient estimates. When multicollinearity is present, it becomes difficult to determine the individual effect of each predictor on the outcome variable because the predictors may provide redundant information.

As a result, the standard errors of the coefficient estimates increase, making it challenging to assess the significance of individual predictors. This can lead to a situation where coefficients may flip signs with different data samples or other seeming inconsistencies that complicate interpretation. Consequently, while the overall fit of the model may not be affected as severely (e.g., the R-squared value might remain high), the estimates of the coefficients themselves are not dependable, leading to conclusions that can misinform decision-making.