Hi! Probably the best answer

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

{\displaystyle \mathrm {HQC} =-2L_{max}+2k\ln(\ln(n)),\ }

where {\displaystyle L_{max}} is the log-likelihood, k is the number of parameters, and n is the number of observations.

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

Hi Sergei, thanks for your insights into the selection criterion comparisons. I have complied my UR tests with AIC and BIC, and just wanted to get with HQIC as well just for comparisons. I have sorted that manually in Eviews.

Hi! You can increase the precision of any model by introducing a variable weighting by sex, age, height, weight, etc.

After spending a lot of time I found there is not any existing R packing to implement Elliott–Rothenberg–Stock (ERS) unitroot test with Hannan-Quinn (HQC) lag selection criterion... It is something worth developing when I have time.