A Predictive Approach to Model Selection and Multicollinearity
We argue for the adoption of a predictive approach to model specification. Specifically, we derive the difference between means and the ratio of determinants of covariance matrices when a subset of explanatory variables is included or excluded from a regression. For several special cases these measures are shown to be related to widely used tools for studying model specification. Results for a set of simulated data and for two economic applications are presented as examples.
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