As the x-variables become more strongly correlated, it becomes more difficult to determine which of the variables are actually producing the statistical effect on the y-variable. This is the problem with multicollinearity.

One way of assessing multicollinearity is using the estat vce command, with the corr (short for correlation) option.

More information help estat vce

Practical example

The first step is re-run the multiple multinomial regression model. The quietly option is included in the beginning of the command to suppress the output.

quietly mlogit marstat40 gpa sex ib1.educ if pop_multinom==1, rrr b(1)

Next, we try the estat vce command. By adding the corr (=correlation) option, we will get a correlation matrix instead of a covariance matrix.

estat vce, corr

The table is too extensive to be pasted here. But per usual, we go through the coefficients and see if there are any strong correlations between the variables/categories (see Correlation analysis).