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In statistics, the Hannan–Quinn information criterion (HQC) is a criterion for model selection. It is an alternative to Akaike information criterion (AIC) and Bayesian information criterion (BIC). It is given as

$\mathrm {HQC} =n\log \left({\mathrm {RSS} \over n}\right)+2k\log \log n,\$

where k is the number of parameters, n is the number of observations, and RSS is the residual sum of squares that results from linear regression or other statistical model.

Burnham & Anderson (2002) say that HQC, "while often cited, seems to have seen little use in practice" (p. 287). They also note that HQC, like BIC, but unlike AIC, is not an estimator of Kullback–Leibler divergence. Claeskens & Hjort (2008) note that HQC, like BIC, but unlike AIC, is not asymptotically efficient (Ch. 4), and further point out that whatever method is being used for fine-tuning the criterion will be more important in practice than the term log log n, since this latter number is small even for very large n.

References

• Aznar Grasa, A. (1989). Econometric Model Selection: A New Approach, Springer. ISBN 978-0-7923-0321-3
• Burnham, K.P. and Anderson, D.R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. Springer-Verlag. ISBN 0-387-95364-7. [This has over 10,000 citations on Google Scholar.]
• Claeskens, G. and Hjort, N.L. (2008). Model Selection and Model Averaging, Cambridge.
• Hannan, E. J., and B. G. Quinn (1979) The Determination of the Order of an Autoregression, Journal of the Royal Statistical Society, B, 41, 190–195.