Introduction to Applied Econometrics Analysis Using Stata by Justin Doran Jane Bourke & Ann Kirby

Author:Justin Doran, Jane Bourke & Ann Kirby
Language: eng
Format: epub
Publisher: Oak Tree Press

TABLE 9

Some output has been omitted from the tables to save space; however, the full tables can be generated using the Do files available on www.justindoran.ie/ebook.html.

We can note that, following the regression estimation presented in Table 8, the VIF in Table 9 indicates that the mean VIF for the model is 3.16. As this is less than 5, it indicates that multicollinearity is not a problem in our model. However, if we look at the individual VIFs, some values are in excess of 5 – for instance, _Ischl_21 (which is a dummy variable representing whether an individual had a Bachelor’s degree) is 17.72. This suggests that this variable may cause problems. However, it does not make sense to drop this variable due to the nature of the dummy variables included. Therefore, we leave this variable in the model and do not adjust the model.

Key Learning Outcomes

What is multicollinearity.

What are the implications of multicollinearity for regression analysis.

How to identify whether multicollinearity is present in a model.

How to correct for multicollinearity in a regression model.

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