As part of the output for most regression procedures, EViews reports various information criteria. The information criteria are often used as a guide in model selection (see for example, Grasa 1989).

The Kullback-Leibler quantity of information contained in a model is the distance from the “true” model and is measured by the log likelihood function. The notion of an information criterion is to provide a measure of information that strikes a balance between this measure of goodness of fit and parsimonious specification of the model. The various information criteria differ in how to strike this balance.