Hannan-Quinn information criterion

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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.

In statistics, the Hannan-Quinn information criterion (HQC) is an alternative to Akaike Information Criterion (AIC) and Bayesian information criterion (BIC). It is given as

where k is the number of parameters, n is the number of observations and RSS is the fitted residual sum of squares of a linear regression.