Which of the following is true regarding multicollinearity?

Multicollinearity refers to a situation in regression analysis where two or more independent variables are highly correlated, making it challenging to estimate the relationship between each independent variable and the dependent variable accurately.

The statement regarding inflated estimates of standard errors used for inference is true. When multicollinearity is present, it can cause the estimates of the regression coefficients to become unstable and less reliable. It inflates the standard errors of the coefficients, which can lead to nonsignificant results even when the independent variables may have a true effect on the dependent variable. This issue makes it difficult to ascertain the proper significance of predictors, as high standard errors can result in wider confidence intervals.

Variance inflation factors (VIF) mentioned in one of the other statements are indeed used to detect multicollinearity, indicating that the second option is also correct. However, the inflated estimates of standard errors being a direct consequence of multicollinearity positions it as a critical issue in the interpretation of regression results. Understanding how multicollinearity influences standard errors is essential for effective statistical modeling and accurate interpretation of results in risk modeling contexts.