Hannan-Quinn information criterion

Information criteria are often used as a guide in model selection (see forexample, 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 informationcriterion is to provide a measure of information that strikes a balance between this measureof goodness of fit and parsimonious specification of the model. The various informationcriteria 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

: $mathrm\{HQC\}\; =\; n\; ln\; left(\; \{mathrm\{RSS\}\; over\; n\; \}\; ight)\; +\; 2\; k\; ln\; ln(n).$

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.