The R command cov2cor(vcov(fitted_model)) will return you the covariance matrix of regression estimates. It is proportional to $(X'X)^{-1}$, which means that in the extreme case of a perfect correlation of a slope and an intercept the covariance matrix is rank deficient. Hence, the matrix $X'X$ doesn't exist, which is impossible, which is a definition of perfect multicollinearity (PM). PM can be problematic for inference, but often is no big deal for forecasting

The R command cov2cor(vcov(fitted_model)) will return you the covariance matrix of regression estimates. It is proportional to $(X'X)^{-1}$, which means that in the extreme case of a perfect correlation of a slope and an intercept the covariance matrix is rank deficient.

Because the inverse of rank deficient matrix doesn't exist, the only way to have this situation is if when the matrix $X'X$ was rank deficient to start with, which is a definition of perfect multicollinearity (PM). PM can be problematic for inference, but often is no big deal for forecasting

The R command cov2cor(vcov(fitted_model)) will return you the covariance matrix of regression estimates. It is proportional to $(X'X)^{-1}$, which means that in the extreme case of a perfect correlation of a slope and an intercept the covariance matrix is rank deficient. Hence, rank deficient is the matrix $X'X$, which is a definition of perfect multicollinearity (PM). PM can be problematic for inference, but often is no big deal for forecasting

The R command cov2cor(vcov(fitted_model)) will return you the covariance matrix of regression estimates. It is proportional to $(X'X)^{-1}$, which means that in the extreme case of a perfect correlation of a slope and an intercept the covariance matrix is rank deficient. Hence, the matrix $X'X$ doesn't exist, which is impossible, which is a definition of perfect multicollinearity (PM). PM can be problematic for inference, but often is no big deal for forecasting