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 Poisson regression model. The quietly option is included in the beginning of the command to suppress the output.

quietly poisson children siblings sex ib1.educ if pop_poisson==1, irr

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 shows the correlations between the different variables/categories. In line with the earlier sections on correlation analysis (see Correlation analysis), we can conclude that the coefficients suggest (very) weak correlations here. The only exceptions are two of the dummies for educ, which is not a huge problem since they reflect the same underlying variable.